{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "OoQxh3aneS5M"
   },
   "source": [
    "# Theme 2 - Climate Variability, Ocean Circulation, and Ecosystem\n",
    "## Climate Variability and Ecosystems\n",
    "OOI Data Labs Education Nuggets\n",
    "\n",
    "_Written by Lori Garzio, Rutgers University_\n",
    "\n",
    "_Disclaimer: data used in this example were downloaded on Oct 18, 2019. The file format and/or contents could have changed._\n",
    "\n",
    "**Objective**: Demonstrate how to download CTD data from multiple instruments on one OOI Global Flanking Mooring using the Machine-to-Machine (M2M) interface, remove outliers, calculate hourly averages, and export the data as a .csv file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 127
    },
    "colab_type": "code",
    "id": "Ey4nt43pq-yt",
    "outputId": "3ffbaf02-acf3-4816-bb4b-2840382527d8"
   },
   "outputs": [],
   "source": [
    "# Notebook Setup\n",
    "import requests\n",
    "import os\n",
    "import re\n",
    "import xarray as xr\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "t1ZezcZjeoUd"
   },
   "source": [
    "## 1. Request Data from OOINet\n",
    "\n",
    "* **Global Irminger Sea** - CTD data from Flanking Mooring B\n",
    "    * Instruments:\n",
    "        * GI03FLMB-RIM01-02-CTDMOG060 (30m)\n",
    "        * GI03FLMB-RIM01-02-CTDMOG065 (180m)\n",
    "        * GI03FLMB-RIM01-02-CTDMOH071 (1,500m)\n",
    "    * Time range: 2014-09-16 to 2017-08-10 (Deployments 1-3)\n",
    "    * Delivery method: recovered_inst\n",
    "    * Data stream: ctdmo_ghqr_instrument_recovered\n",
    "    * Parameters: ctdmo_seawater_pressure, ctdmo_seawater_temperature, practical_salinity, density"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **request_data** function below sends data requests with inputs specified by the user to OOINet and returns the THREDDs urls where the downloaded data files can be found."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# enter your OOI API username and token, and directory where output .csv files are saved\n",
    "API_USERNAME = ''\n",
    "API_TOKEN = ''\n",
    "save_dir = '/Users/lgarzio/Documents/OOI/Nuggets/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "B8cTVmt5mhsA"
   },
   "outputs": [],
   "source": [
    "def request_data(reference_designator, method, stream, start_date=None, end_date=None):\n",
    "    site = reference_designator[:8]\n",
    "    node = reference_designator[9:14]\n",
    "    instrument = reference_designator[15:]\n",
    "\n",
    "    # Create the request URL\n",
    "    api_base_url = 'https://ooinet.oceanobservatories.org/api/m2m/12576/sensor/inv'\n",
    "    data_request_url = '/'.join((api_base_url, site, node, instrument, method, stream))\n",
    "\n",
    "    # All of the following are optional, but you should specify a date range\n",
    "    params = {\n",
    "        'format': 'application/netcdf',\n",
    "        'include_provenance': 'true',\n",
    "        'include_annotations': 'true'\n",
    "    }\n",
    "    if start_date:\n",
    "        params['beginDT'] = start_date\n",
    "    if end_date:\n",
    "        params['endDT'] = end_date\n",
    "\n",
    "    # Make the data request\n",
    "    r = requests.get(data_request_url, params=params, auth=(API_USERNAME, API_TOKEN))\n",
    "    data = r.json()\n",
    "\n",
    "    # Return just the THREDDS URL\n",
    "    return data['allURLs'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, use that **request_data** function to download data for the three instruments of interest. You only need to do this once! These lines are commented out to prevent accidental re-submission of data requests."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GDx1obDmm11u"
   },
   "outputs": [],
   "source": [
    "# GI03FLMB60_url = request_data('GI03FLMB-RIM01-02-CTDMOG060', 'recovered_inst', 'ctdmo_ghqr_instrument_recovered',\n",
    "#                             '2014-09-16T00:00:00.000Z', '2017-08-10T00:00:00.000Z')\n",
    "# \n",
    "# GI03FLMB65_url = request_data('GI03FLMB-RIM01-02-CTDMOG065', 'recovered_inst', 'ctdmo_ghqr_instrument_recovered',\n",
    "#                             '2014-09-16T00:00:00.000Z', '2017-08-10T00:00:00.000Z')\n",
    "# \n",
    "# GI03FLMB71_url = request_data('GI03FLMB-RIM01-02-CTDMOH071', 'recovered_inst', 'ctdmo_ghqr_instrument_recovered',\n",
    "#                             '2014-09-16T00:00:00.000Z', '2017-08-10T00:00:00.000Z')\n",
    "# print('GI03FLMB-RIM01-02-CTDMOG060 url = %s' %GI03FLMB60_url)\n",
    "# print('GI03FLMB-RIM01-02-CTDMOG065 url = %s' %GI03FLMB65_url)\n",
    "# print('GI03FLMB-RIM01-02-CTDMOH071 url =  %s' %GI03FLMB71_url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UX_TNzTqephc"
   },
   "source": [
    "## 2. Load Data Files\n",
    "\n",
    "Copy the links to the THREDDs catalog above that resulted from the **request_data** function (to avoid re-requesting the data). Note: the urls must be surrounded by quotations. Depending on the data request, it may take several minutes for the request to fulfill. The request is complete when you receive an email from the system with the link to your data, and a status.txt file shows up in the THREDDs catalog that says \"Complete\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_zkTLF5gtPZx"
   },
   "outputs": [],
   "source": [
    "CTD30m_url = 'https://opendap.oceanobservatories.org/thredds/catalog/ooi/lgarzio@marine.rutgers.edu/20191018T145750777Z-GI03FLMB-RIM01-02-CTDMOG060-recovered_inst-ctdmo_ghqr_instrument_recovered/catalog.html'\n",
    "CTD180m_url = 'https://opendap.oceanobservatories.org/thredds/catalog/ooi/lgarzio@marine.rutgers.edu/20191018T145751205Z-GI03FLMB-RIM01-02-CTDMOG065-recovered_inst-ctdmo_ghqr_instrument_recovered/catalog.html'\n",
    "CTD1500m_url = 'https://opendap.oceanobservatories.org/thredds/catalog/ooi/lgarzio@marine.rutgers.edu/20191018T145751575Z-GI03FLMB-RIM01-02-CTDMOH071-recovered_inst-ctdmo_ghqr_instrument_recovered/catalog.html'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **get_data** function below:\n",
    "* selects the appropriate datasets in a THREDDs catalog by selecting NetCDF files, removing collocated datasets (if provided), and removing deployments that aren't specified (if provided)\n",
    "* creates an empty dictionary with placeholders to populate with data just for variables of interest\n",
    "* creates another dictionary to store the units for the variables of interest\n",
    "* opens the dataset(s), extracts the data and units for the variables of interest and adds them to the dictionaries\n",
    "* converts the data dictionary to a dataframe\n",
    "* returns the dataframe and the dictionary containing the variable units. \n",
    "\n",
    "This type of function is most useful when working with multiple large/dense datasets, but is also fine for working with smaller datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CIvFhvqdgBni"
   },
   "outputs": [],
   "source": [
    "def get_data(url, variables, deployments=None):\n",
    "    # Function to grab all data from specified directory\n",
    "    tds_url = 'https://opendap.oceanobservatories.org/thredds/dodsC'\n",
    "    dataset = requests.get(url).text\n",
    "    ii = re.findall(r'href=[\\'\"]?([^\\'\" >]+)', dataset)\n",
    "    # x = re.findall(r'(ooi/.*?.nc)', dataset)\n",
    "    x = [y for y in ii if y.endswith('.nc')]\n",
    "    for i in x:\n",
    "        if i.endswith('.nc') == False:\n",
    "            x.remove(i)\n",
    "    for i in x:\n",
    "        try:\n",
    "            float(i[-4])\n",
    "        except:\n",
    "            x.remove(i)\n",
    "    # dataset = [os.path.join(tds_url, i) for i in x]\n",
    "    datasets = [os.path.join(tds_url, i.split('=')[-1]) for i in x]\n",
    "\n",
    "    # remove deployments not in deployment list, if given\n",
    "    if deployments is not None:\n",
    "        deploy = ['deployment{:04d}'.format(j) for j in deployments]\n",
    "        datasets = [k for k in datasets if k.split('/')[-1].split('_')[0] in deploy]\n",
    "\n",
    "    # remove collocated data files if necessary\n",
    "    catalog_rms = url.split('/')[-2][20:]\n",
    "    selected_datasets = []\n",
    "    for d in datasets:\n",
    "        if catalog_rms == d.split('/')[-1].split('_20')[0][15:]:\n",
    "            selected_datasets.append(d)\n",
    "\n",
    "    # create a dictionary to populate with data from the selected datasets\n",
    "    data_dict = {'time': np.array([], dtype='datetime64[ns]')}\n",
    "    unit_dict = {}\n",
    "    for v in variables:\n",
    "        data_dict.update({v: np.array([])})\n",
    "        unit_dict.update({v: []})\n",
    "    print('Appending data from files')\n",
    "\n",
    "    for sd in selected_datasets:\n",
    "        ds = xr.open_dataset(sd, mask_and_scale=False)\n",
    "        data_dict['time'] = np.append(data_dict['time'], ds['time'].values)\n",
    "        for var in variables:\n",
    "            data_dict[var] = np.append(data_dict[var], ds[var].values)\n",
    "            units = ds[var].units\n",
    "            if units not in unit_dict[var]:\n",
    "                unit_dict[var].append(units)\n",
    "\n",
    "    # convert dictionary to a dataframe\n",
    "    df = pd.DataFrame(data_dict)\n",
    "    df.sort_values(by=['time'], inplace=True)  # make sure the timestamps are in ascending order\n",
    "\n",
    "    return df, unit_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can specify the variables of interest and get the data for the three datasets. An option in the get_data function is to specify deployments (dps), and we only want data from deployments 1, 2, and 3 for this exercise. The global mooring deployments overlap, sometimes by as much as two weeks. So even though we only requested data through the end date of deployment 3 (Aug 11, 2017), we will also receive a NetCDF file containing data from the beginning of deployment 4 because that deployment started on Aug 7, 2017. We can filter that deployment 4 file out of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "L4dTLdWxgDU4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Appending data from files\n",
      "Appending data from files\n",
      "Appending data from files\n",
      "{'ctdmo_seawater_pressure': ['dbar'], 'ctdmo_seawater_temperature': ['ºC'], 'practical_salinity': ['1'], 'density': ['kg m-3']}\n"
     ]
    }
   ],
   "source": [
    "# Specify the variable(s) of interest and get the data for the three datasets\n",
    "variables = ['ctdmo_seawater_pressure', 'ctdmo_seawater_temperature', 'practical_salinity', 'density']\n",
    "dps = [1, 2, 3]  # deployments to keep\n",
    "\n",
    "CTD30m_data, CTD30m_units = get_data(CTD30m_url, variables, dps)\n",
    "CTD180m_data, CTD180m_units = get_data(CTD180m_url, variables, dps)\n",
    "CTD1500m_data, CTD1500m_units = get_data(CTD1500m_url, variables, dps)\n",
    "print(CTD1500m_units)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "iqBxQMaPOKlO"
   },
   "source": [
    "## Quick Data Plots\n",
    "Make quick plots to make sure you downloaded the correct data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 876
    },
    "colab_type": "code",
    "id": "0-XCeqpfpc1O",
    "outputId": "2e8c93d2-27a4-470b-b27c-17c8a84d579f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the pressure data to make sure you have the CTDs you were expecting\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(CTD30m_data['time'], CTD30m_data['ctdmo_seawater_pressure'], '.', label='30m')\n",
    "plt.plot(CTD180m_data['time'], CTD180m_data['ctdmo_seawater_pressure'], '.', label='180m')\n",
    "plt.plot(CTD1500m_data['time'], CTD1500m_data['ctdmo_seawater_pressure'], '.', label='1500m')\n",
    "ax.legend()\n",
    "ax.invert_yaxis()\n",
    "ax.set_xlabel('')\n",
    "ax.set_ylabel('Seawater Pressure ({})'.format(CTD30m_units['ctdmo_seawater_pressure'][0]))\n",
    "ax.set_title('Irminger Flanking Mooring B: Deployments 1-3')\n",
    "plt.xticks(rotation=35);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a few outliers scattered throughout these datasets, usually concentrated near the deployment start/end times. Because these moorings are in very deep waters, it can take several hours for the mooring to completely settle and those data points are most likely captured as the instruments are sinking to their final deployment depth. Similarly, at recovery some data points can be captured as the instruments rise in the water column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 549
    },
    "colab_type": "code",
    "id": "65N5_a1IRbSG",
    "outputId": "ba457ecf-1ca5-42d1-c17d-252d96ccace4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make a quick plot of temperature\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(CTD30m_data['time'], CTD30m_data['ctdmo_seawater_temperature'], '.', label='30m')\n",
    "plt.plot(CTD180m_data['time'], CTD180m_data['ctdmo_seawater_temperature'], '.', label='180m')\n",
    "plt.plot(CTD1500m_data['time'], CTD1500m_data['ctdmo_seawater_temperature'], '.', label='1500m')\n",
    "ax.legend()\n",
    "ax.set_xlabel('')\n",
    "ax.set_ylabel('Seawater Temperature ({})'.format(CTD30m_units['ctdmo_seawater_temperature'][0]))\n",
    "ax.set_title('Irminger Flanking Mooring B: Deployments 1-3')\n",
    "plt.xticks(rotation=35);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ...and salinity - the outliers here at the beginning of each deployment are very obvious\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(CTD30m_data['time'], CTD30m_data['practical_salinity'], '.', label='30m')\n",
    "plt.plot(CTD180m_data['time'], CTD180m_data['practical_salinity'], '.', label='180m')\n",
    "plt.plot(CTD1500m_data['time'], CTD1500m_data['practical_salinity'], '.', label='1500m')\n",
    "ax.legend()\n",
    "ax.set_xlabel('')\n",
    "ax.set_ylabel('Practical Salinity ({})'.format(CTD30m_units['practical_salinity'][0]))\n",
    "ax.set_title('Irminger Flanking Mooring B: Deployments 1-3')\n",
    "plt.xticks(rotation=35);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ...and density - again, the outliers are very obvious\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(CTD30m_data['time'], CTD30m_data['density'], '.', label='30m')\n",
    "plt.plot(CTD180m_data['time'], CTD180m_data['density'], '.', label='180m')\n",
    "plt.plot(CTD1500m_data['time'], CTD1500m_data['density'], '.', label='1500m')\n",
    "ax.legend()\n",
    "ax.set_xlabel('')\n",
    "ax.set_ylabel('Seawater Density ({})'.format(CTD30m_units['density'][0]))\n",
    "ax.set_title('Irminger Flanking Mooring B: Deployments 1-3')\n",
    "plt.xticks(rotation=35);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 621
    },
    "colab_type": "code",
    "id": "vEmA6lC3gHWx",
    "outputId": "f7e79a9d-d2ca-4880-90ca-864f38ca3fb9"
   },
   "source": [
    "We'll want to remove those outliers before calculating hourly averages (to make the csv files more manageable). Here's a function to remove the outliers from the dataframe (it removes the entire row of data if one variable is an outlier)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_outliers(df, variables, m):\n",
    "    cnames = []\n",
    "    for v in variables:\n",
    "        mn = np.nanmean(df[v])\n",
    "        std = np.nanstd(df[v])\n",
    "        cname = v + '_ind'\n",
    "        cnames.append(cname)\n",
    "        df[cname] = abs(df[v] - mn) < m * std\n",
    "    for cn in cnames:\n",
    "        df = df.loc[df[cn] == True]\n",
    "    df = df.drop(columns=cnames)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Specify the # of standard deviations from the mean you want to remove. In this example we'll remove any data points that are more than 5 standard deviations from the mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "std_from_mean = 5\n",
    "CTD30m_data_clean = remove_outliers(CTD30m_data, variables, std_from_mean)\n",
    "CTD180m_data_clean = remove_outliers(CTD180m_data, variables, std_from_mean)\n",
    "CTD1500m_data_clean = remove_outliers(CTD1500m_data, variables, std_from_mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the salinity and density data again to make sure you removed all of the outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "plt.plot(CTD30m_data_clean['time'], CTD30m_data_clean['practical_salinity'], '.', label='30m')\n",
    "plt.plot(CTD180m_data_clean['time'], CTD180m_data_clean['practical_salinity'], '.', label='180m')\n",
    "plt.plot(CTD1500m_data_clean['time'], CTD1500m_data_clean['practical_salinity'], '.', label='1500m')\n",
    "ax.legend()\n",
    "ax.set_xlabel('')\n",
    "ax.set_ylabel('Practical Salinity ({})'.format(CTD30m_units['practical_salinity'][0]))\n",
    "ax.set_title('Irminger Flanking Mooring B: Deployments 1-3 (outliers removed)')\n",
    "plt.xticks(rotation=35);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "plt.plot(CTD30m_data_clean['time'], CTD30m_data_clean['density'], '.', label='30m')\n",
    "plt.plot(CTD180m_data_clean['time'], CTD180m_data_clean['density'], '.', label='180m')\n",
    "plt.plot(CTD1500m_data_clean['time'], CTD1500m_data_clean['density'], '.', label='1500m')\n",
    "ax.legend()\n",
    "ax.set_xlabel('')\n",
    "ax.set_ylabel('Seawater Density ({})'.format(CTD30m_units['density'][0]))\n",
    "ax.set_title('Irminger Flanking Mooring B: Deployments 1-3 (outliers removed)')\n",
    "plt.xticks(rotation=35);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks much better! Now we can calculate hourly averages (to make the dataset more manageable), merge the datasets, and export as a .csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1MMalPTQerEa"
   },
   "source": [
    "## 3. Merge and export"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "f1uV_LLRFTtb"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>time</th>\n",
       "      <th>CTD30m Pressure (dbar)</th>\n",
       "      <th>CTD30m Temp (deg_C)</th>\n",
       "      <th>CTD30m Salinity (1)</th>\n",
       "      <th>CTD30m Density (kg m-3)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>69469</th>\n",
       "      <td>2014-09-16 14:00:01</td>\n",
       "      <td>28.828550</td>\n",
       "      <td>7.984068</td>\n",
       "      <td>34.810886</td>\n",
       "      <td>1027.262175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69470</th>\n",
       "      <td>2014-09-16 14:15:01</td>\n",
       "      <td>26.983611</td>\n",
       "      <td>7.985942</td>\n",
       "      <td>34.810388</td>\n",
       "      <td>1027.253073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69471</th>\n",
       "      <td>2014-09-16 14:30:01</td>\n",
       "      <td>27.674246</td>\n",
       "      <td>7.981017</td>\n",
       "      <td>34.809898</td>\n",
       "      <td>1027.256583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69472</th>\n",
       "      <td>2014-09-16 14:45:01</td>\n",
       "      <td>27.543585</td>\n",
       "      <td>7.978233</td>\n",
       "      <td>34.810373</td>\n",
       "      <td>1027.256775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69473</th>\n",
       "      <td>2014-09-16 15:00:01</td>\n",
       "      <td>27.425369</td>\n",
       "      <td>7.977859</td>\n",
       "      <td>34.812018</td>\n",
       "      <td>1027.257584</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     time  CTD30m Pressure (dbar)  CTD30m Temp (deg_C)  \\\n",
       "69469 2014-09-16 14:00:01               28.828550             7.984068   \n",
       "69470 2014-09-16 14:15:01               26.983611             7.985942   \n",
       "69471 2014-09-16 14:30:01               27.674246             7.981017   \n",
       "69472 2014-09-16 14:45:01               27.543585             7.978233   \n",
       "69473 2014-09-16 15:00:01               27.425369             7.977859   \n",
       "\n",
       "       CTD30m Salinity (1)  CTD30m Density (kg m-3)  \n",
       "69469            34.810886              1027.262175  \n",
       "69470            34.810388              1027.253073  \n",
       "69471            34.809898              1027.256583  \n",
       "69472            34.810373              1027.256775  \n",
       "69473            34.812018              1027.257584  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rename and add units to the columns\n",
    "CTD30m_data_clean = CTD30m_data_clean.rename(columns={'ctdmo_seawater_pressure': 'CTD30m Pressure ({})'.format(CTD30m_units['ctdmo_seawater_pressure'][0]),\n",
    "                                                     'ctdmo_seawater_temperature': 'CTD30m Temp (deg_C)',\n",
    "                                                     'practical_salinity': 'CTD30m Salinity ({})'.format(CTD30m_units['practical_salinity'][0]),\n",
    "                                                     'density': 'CTD30m Density ({})'.format(CTD30m_units['density'][0])})\n",
    "\n",
    "CTD180m_data_clean = CTD180m_data_clean.rename(columns={'ctdmo_seawater_pressure': 'CTD180m Pressure ({})'.format(CTD180m_units['ctdmo_seawater_pressure'][0]),\n",
    "                                                     'ctdmo_seawater_temperature': 'CTD180m Temp (deg_C)',\n",
    "                                                     'practical_salinity': 'CTD180m Salinity ({})'.format(CTD180m_units['practical_salinity'][0]),\n",
    "                                                     'density': 'CTD180m Density ({})'.format(CTD180m_units['density'][0])})\n",
    "\n",
    "CTD1500m_data_clean = CTD1500m_data_clean.rename(columns={'ctdmo_seawater_pressure': 'CTD1500m Pressure ({})'.format(CTD1500m_units['ctdmo_seawater_pressure'][0]),\n",
    "                                                     'ctdmo_seawater_temperature': 'CTD1500m Temp (deg_C)',\n",
    "                                                     'practical_salinity': 'CTD1500m Salinity ({})'.format(CTD1500m_units['practical_salinity'][0]),\n",
    "                                                     'density': 'CTD1500m Density ({})'.format(CTD1500m_units['density'][0])})\n",
    "\n",
    "# print the first few lines of the 30m dataset\n",
    "CTD30m_data_clean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>time</th>\n",
       "      <th>CTD30m Pressure (dbar)</th>\n",
       "      <th>CTD30m Temp (deg_C)</th>\n",
       "      <th>CTD30m Salinity (1)</th>\n",
       "      <th>CTD30m Density (kg m-3)</th>\n",
       "      <th>CTD180m Pressure (dbar)</th>\n",
       "      <th>CTD180m Temp (deg_C)</th>\n",
       "      <th>CTD180m Salinity (1)</th>\n",
       "      <th>CTD180m Density (kg m-3)</th>\n",
       "      <th>CTD1500m Pressure (dbar)</th>\n",
       "      <th>CTD1500m Temp (deg_C)</th>\n",
       "      <th>CTD1500m Salinity (1)</th>\n",
       "      <th>CTD1500m Density (kg m-3)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-09-16 13:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1539.099718</td>\n",
       "      <td>3.508319</td>\n",
       "      <td>34.925264</td>\n",
       "      <td>1034.849791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-09-16 14:00:00</td>\n",
       "      <td>27.757498</td>\n",
       "      <td>7.982315</td>\n",
       "      <td>34.810386</td>\n",
       "      <td>1027.257152</td>\n",
       "      <td>181.288108</td>\n",
       "      <td>4.509500</td>\n",
       "      <td>34.912405</td>\n",
       "      <td>1028.504372</td>\n",
       "      <td>1511.721559</td>\n",
       "      <td>3.514592</td>\n",
       "      <td>34.924908</td>\n",
       "      <td>1034.724884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-09-16 15:00:00</td>\n",
       "      <td>27.349080</td>\n",
       "      <td>7.972829</td>\n",
       "      <td>34.811432</td>\n",
       "      <td>1027.257528</td>\n",
       "      <td>180.953329</td>\n",
       "      <td>4.603614</td>\n",
       "      <td>34.928709</td>\n",
       "      <td>1028.504909</td>\n",
       "      <td>1511.637069</td>\n",
       "      <td>3.517438</td>\n",
       "      <td>34.925069</td>\n",
       "      <td>1034.724241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-09-16 16:00:00</td>\n",
       "      <td>26.873949</td>\n",
       "      <td>7.964376</td>\n",
       "      <td>34.812645</td>\n",
       "      <td>1027.257575</td>\n",
       "      <td>180.433915</td>\n",
       "      <td>4.406867</td>\n",
       "      <td>34.909062</td>\n",
       "      <td>1028.509529</td>\n",
       "      <td>1511.099686</td>\n",
       "      <td>3.534492</td>\n",
       "      <td>34.928499</td>\n",
       "      <td>1034.722186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-09-16 17:00:00</td>\n",
       "      <td>26.346640</td>\n",
       "      <td>7.967358</td>\n",
       "      <td>34.814620</td>\n",
       "      <td>1027.256271</td>\n",
       "      <td>179.940000</td>\n",
       "      <td>4.545330</td>\n",
       "      <td>34.921320</td>\n",
       "      <td>1028.501101</td>\n",
       "      <td>1510.611324</td>\n",
       "      <td>3.532637</td>\n",
       "      <td>34.925361</td>\n",
       "      <td>1034.717767</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 time  CTD30m Pressure (dbar)  CTD30m Temp (deg_C)  \\\n",
       "0 2014-09-16 13:00:00                     NaN                  NaN   \n",
       "1 2014-09-16 14:00:00               27.757498             7.982315   \n",
       "2 2014-09-16 15:00:00               27.349080             7.972829   \n",
       "3 2014-09-16 16:00:00               26.873949             7.964376   \n",
       "4 2014-09-16 17:00:00               26.346640             7.967358   \n",
       "\n",
       "   CTD30m Salinity (1)  CTD30m Density (kg m-3)  CTD180m Pressure (dbar)  \\\n",
       "0                  NaN                      NaN                      NaN   \n",
       "1            34.810386              1027.257152               181.288108   \n",
       "2            34.811432              1027.257528               180.953329   \n",
       "3            34.812645              1027.257575               180.433915   \n",
       "4            34.814620              1027.256271               179.940000   \n",
       "\n",
       "   CTD180m Temp (deg_C)  CTD180m Salinity (1)  CTD180m Density (kg m-3)  \\\n",
       "0                   NaN                   NaN                       NaN   \n",
       "1              4.509500             34.912405               1028.504372   \n",
       "2              4.603614             34.928709               1028.504909   \n",
       "3              4.406867             34.909062               1028.509529   \n",
       "4              4.545330             34.921320               1028.501101   \n",
       "\n",
       "   CTD1500m Pressure (dbar)  CTD1500m Temp (deg_C)  CTD1500m Salinity (1)  \\\n",
       "0               1539.099718               3.508319              34.925264   \n",
       "1               1511.721559               3.514592              34.924908   \n",
       "2               1511.637069               3.517438              34.925069   \n",
       "3               1511.099686               3.534492              34.928499   \n",
       "4               1510.611324               3.532637              34.925361   \n",
       "\n",
       "   CTD1500m Density (kg m-3)  \n",
       "0                1034.849791  \n",
       "1                1034.724884  \n",
       "2                1034.724241  \n",
       "3                1034.722186  \n",
       "4                1034.717767  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate hourly averages for each dataframe\n",
    "CTD30m_hourly = CTD30m_data_clean.resample('H', on='time').mean().dropna(how='all').reset_index()\n",
    "CTD180m_hourly = CTD180m_data_clean.resample('H', on='time').mean().dropna(how='all').reset_index()\n",
    "CTD1500m_hourly = CTD1500m_data_clean.resample('H', on='time').mean().dropna(how='all').reset_index()\n",
    "\n",
    "# merge the dataframes\n",
    "merge1 = pd.merge(CTD30m_hourly, CTD180m_hourly, on='time', how='outer')\n",
    "merged = pd.merge(merge1, CTD1500m_hourly, on='time', how='outer')\n",
    "merged = merged.sort_values('time').reset_index(drop=True)  # make sure the dataframe is sorted by time\n",
    "\n",
    "merged.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have our hourly-averaged, merged dataframe, let's plot the variables to make sure it looks reasonable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x1008 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1,ax2,ax3,ax4) = plt.subplots(4,1, sharex=True, figsize = (8,14))\n",
    "ax1.plot(merged['time'], merged['CTD30m Pressure (dbar)'], '.', label='30m')\n",
    "ax1.plot(merged['time'], merged['CTD180m Pressure (dbar)'], '.', label='180m')\n",
    "ax1.plot(merged['time'], merged['CTD1500m Pressure (dbar)'], '.', label='1500m')\n",
    "ax1.invert_yaxis()\n",
    "ax1.legend()\n",
    "ax1.set_xlabel('')\n",
    "ax1.set_ylabel('Seawater Pressure (dbar)')\n",
    "ax1.set_title('Irminger Flanking Mooring B: Deployments 1-3 (hourly average)')\n",
    "\n",
    "ax2.plot(merged['time'], merged['CTD30m Temp (deg_C)'], '.', label='30m')\n",
    "ax2.plot(merged['time'], merged['CTD180m Temp (deg_C)'], '.', label='180m')\n",
    "ax2.plot(merged['time'], merged['CTD1500m Temp (deg_C)'], '.', label='1500m')\n",
    "ax2.legend()\n",
    "ax2.set_xlabel('')\n",
    "ax2.set_ylabel('Seawater Temperature (deg_C)')\n",
    "\n",
    "ax3.plot(merged['time'], merged['CTD30m Salinity (1)'], '.', label='30m')\n",
    "ax3.plot(merged['time'], merged['CTD180m Salinity (1)'], '.', label='180m')\n",
    "ax3.plot(merged['time'], merged['CTD1500m Salinity (1)'], '.', label='1500m')\n",
    "ax3.legend()\n",
    "ax3.set_xlabel('')\n",
    "ax3.set_ylabel('Practical Salinity')\n",
    "\n",
    "ax4.plot(merged['time'], merged['CTD30m Density (kg m-3)'], '.', label='30m')\n",
    "ax4.plot(merged['time'], merged['CTD180m Density (kg m-3)'], '.', label='180m')\n",
    "ax4.plot(merged['time'], merged['CTD1500m Density (kg m-3)'], '.', label='1500m')\n",
    "ax4.legend()\n",
    "ax4.set_xlabel('')\n",
    "ax4.set_ylabel('Seawater Density (kg m-3)');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Looks good! Export to csv\n",
    "merged.to_csv(os.path.join(save_dir, 'Theme2a_Irminger_CTDs.csv'), index=False)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "DL June Air-Sea v1.ipynb",
   "provenance": [],
   "toc_visible": true,
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}