Test1 REF - OPeNDAP Xarray use cases.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CTD 2003 Subset - In memory analysis with Xarray\n",
    "## Introduction\n",
    "This Notebook provides support to application developers, for them to analyse and optimise their online access to NetCDF datasets via queries to an OPeNDAP Server. \n",
    "\n",
    "The test dataset used in this Notebook file was prepared by Terradue out of the IMR CTD products exploited for the EC INTAROS project (https://cordis.europa.eu/project/id/727890).\n",
    "\n",
    "The test dataset has been uploaded on both the OPeNDAP TDS and Hyrax servers, hosted on Terradue Cloud Platform: \n",
    "https://opendap.terradue.com/thredds/dodsC/subset_2003/\n",
    "https://opendap.terradue.com/hyrax/data/subset_2003/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import modules\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pprint\n",
    "import xarray as xr\n",
    "import requests\n",
    "import re\n",
    "\n",
    "from helpers import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": "true"
   },
   "source": [
    "## Set-up"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Server URL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define URLs of servers  \n",
    "hyrax_url = 'https://opendap.terradue.com/hyrax/data/subset_2003/' # on HYRAX server\n",
    "thredds_url = 'https://opendap.terradue.com/thredds/dodsC/subset_2003/' # on THREDDS server\n",
    "\n",
    "# Choose the one you want to use (hyrax_url or thredds_url)\n",
    "server_url = hyrax_url # or thredds_url"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define Year and Platform Codes \n",
    "The information on **year** and **platforms** available on the server (the individual NetCDF files) must be known a priori "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "year = 2003 # Define year of interest\n",
    "platform_codes = ['AA', 'GS', 'GT', 'HJ', 'JH'] # Define codes of platforms of interest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": "true"
   },
   "source": [
    "## Retrieval of DDS info\n",
    "Access data using the dds info, to retrieve the dimensions of the data for each dimensions: 'TIME', 'LATITUDE', 'LONGITUDE', 'DEPTH', 'POSITION'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TIME: {'AA': 683, 'GS': 404, 'GT': 990, 'HJ': 178, 'JH': 949}\n",
      "DEPTH: {'AA': 2809, 'GS': 3683, 'GT': 2956, 'HJ': 789, 'JH': 3763}\n"
     ]
    }
   ],
   "source": [
    "# Get dds info, and assign max dimensions to TIME and DEPTH\n",
    "pc_dim_dict = {}\n",
    "time_stop_dict = {}\n",
    "depth_stop_dict = {}\n",
    "\n",
    "for pc in platform_codes:\n",
    "    \n",
    "    dds = f'{server_url}/58{pc}_CTD_{year}.nc.nc4.dds'\n",
    "    \n",
    "    pc_dim_dict[pc] = retrieveDDSinfo(dds)\n",
    "    \n",
    "    time_stop_dict[pc] = pc_dim_dict[pc]['TIME']\n",
    "    depth_stop_dict[pc] = pc_dim_dict[pc]['DEPTH']\n",
    "\n",
    "#pprint.pprint(pc_dim_dict)\n",
    "print('TIME:', time_stop_dict)\n",
    "print('DEPTH:', depth_stop_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visual Analysis: Load and Plot Positions only\n",
    "The objective of this section is to visualise the geograhical positions of the data for each platform, and to perform some filtering operations based on locations and time queries. This is possible using only the necessary information retrieved from the DDS. The key dimensions that are used for the position analysis are: 'TIME', 'LATITUDE', 'LONGITUDE'. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Position_Dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Platform: AA. URL with Queries: https://opendap.terradue.com/hyrax/data/subset_2003/58AA_CTD_2003.nc.nc4?TIME[0:1:683],LATITUDE[0:1:683],LONGITUDE[0:1:683]\n",
      "Platform: GS. URL with Queries: https://opendap.terradue.com/hyrax/data/subset_2003/58GS_CTD_2003.nc.nc4?TIME[0:1:404],LATITUDE[0:1:404],LONGITUDE[0:1:404]\n",
      "Platform: GT. URL with Queries: https://opendap.terradue.com/hyrax/data/subset_2003/58GT_CTD_2003.nc.nc4?TIME[0:1:990],LATITUDE[0:1:990],LONGITUDE[0:1:990]\n",
      "Platform: HJ. URL with Queries: https://opendap.terradue.com/hyrax/data/subset_2003/58HJ_CTD_2003.nc.nc4?TIME[0:1:178],LATITUDE[0:1:178],LONGITUDE[0:1:178]\n",
      "Platform: JH. URL with Queries: https://opendap.terradue.com/hyrax/data/subset_2003/58JH_CTD_2003.nc.nc4?TIME[0:1:949],LATITUDE[0:1:949],LONGITUDE[0:1:949]\n"
     ]
    }
   ],
   "source": [
    "position_dict = {}\n",
    "\n",
    "for pc in platform_codes:\n",
    "    \n",
    "    coords_str = getQueryString(pc_dim_dict[pc], keylist = ['TIME', 'LATITUDE', 'LONGITUDE']) # list the coordinates you want\n",
    "    \n",
    "    fix_lab = f'58{pc}_CTD_{year}' # label to use for this campaign\n",
    "    \n",
    "    url = f'{server_url}{fix_lab}.nc.nc4?{coords_str}'; print(f'Platform: {pc}. URL with Queries:', url)\n",
    "\n",
    "    remote_data, data_attr = fetch_data(url)\n",
    "    \n",
    "    position_dict[pc] = {'data': remote_data, \n",
    "                         'data_attr': data_attr}\n",
    "    \n",
    "#     print(f'{data_attr}\\n')\n",
    "    \n",
    "# display(position_dict)\n",
    "\n",
    "# print(pc_dim_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Match and merge LAT, LONG and TIME of positions in a *position_df* dataframe\n",
    "Generate a pandas dataframe (*lonlat_df*) to store all locations (**Longitude** and **Latitude**) and respective **Time** for all platforms. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Platform AA: 684 measurement locations.\n"
     ]
    },
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       "      <th>679</th>\n",
       "      <td>9.7812</td>\n",
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       "      <th>680</th>\n",
       "      <td>11.1663</td>\n",
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       "      <th>681</th>\n",
       "      <td>10.3622</td>\n",
       "      <td>63.530300</td>\n",
       "      <td>2003-12-08 17:45:03</td>\n",
       "      <td>AA</td>\n",
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       "      <th>682</th>\n",
       "      <td>10.9007</td>\n",
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       "      <td>AA</td>\n",
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       "      <td>11.0433</td>\n",
       "      <td>63.917198</td>\n",
       "      <td>2003-12-09 06:15:53</td>\n",
       "      <td>AA</td>\n",
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       "     Longitude   Latitude                Time Platform\n",
       "679     9.7812  63.622799 2003-12-07 19:14:05       AA\n",
       "680    11.1663  63.758301 2003-12-08 07:20:41       AA\n",
       "681    10.3622  63.530300 2003-12-08 17:45:03       AA\n",
       "682    10.9007  63.587700 2003-12-08 21:12:53       AA\n",
       "683    11.0433  63.917198 2003-12-09 06:15:53       AA"
      ]
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     "output_type": "display_data"
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     "output_type": "stream",
     "text": [
      "Platform GS: 405 measurement locations.\n"
     ]
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       "      <th>400</th>\n",
       "      <td>15.846300</td>\n",
       "      <td>68.271698</td>\n",
       "      <td>2003-12-12 11:53:50</td>\n",
       "      <td>GS</td>\n",
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       "      <th>401</th>\n",
       "      <td>16.090700</td>\n",
       "      <td>68.362701</td>\n",
       "      <td>2003-12-12 15:59:43</td>\n",
       "      <td>GS</td>\n",
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       "      <th>402</th>\n",
       "      <td>16.091000</td>\n",
       "      <td>68.368500</td>\n",
       "      <td>2003-12-12 16:25:28</td>\n",
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       "      <th>403</th>\n",
       "      <td>16.091999</td>\n",
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       "      <td>GS</td>\n",
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       "      <th>404</th>\n",
       "      <td>16.092300</td>\n",
       "      <td>68.384697</td>\n",
       "      <td>2003-12-12 17:34:18</td>\n",
       "      <td>GS</td>\n",
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       "     Longitude   Latitude                Time Platform\n",
       "400  15.846300  68.271698 2003-12-12 11:53:50       GS\n",
       "401  16.090700  68.362701 2003-12-12 15:59:43       GS\n",
       "402  16.091000  68.368500 2003-12-12 16:25:28       GS\n",
       "403  16.091999  68.376198 2003-12-12 16:56:06       GS\n",
       "404  16.092300  68.384697 2003-12-12 17:34:18       GS"
      ]
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     "text": [
      "Platform GT: 991 measurement locations.\n"
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       "      <th>986</th>\n",
       "      <td>0.5003</td>\n",
       "      <td>57.240501</td>\n",
       "      <td>2003-10-03 22:24:21</td>\n",
       "      <td>GT</td>\n",
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       "      <th>987</th>\n",
       "      <td>0.4992</td>\n",
       "      <td>56.780300</td>\n",
       "      <td>2003-10-04 02:38:20</td>\n",
       "      <td>GT</td>\n",
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       "      <th>988</th>\n",
       "      <td>-0.3618</td>\n",
       "      <td>56.744801</td>\n",
       "      <td>2003-10-04 10:01:20</td>\n",
       "      <td>GT</td>\n",
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       "      <th>989</th>\n",
       "      <td>-0.5513</td>\n",
       "      <td>57.132500</td>\n",
       "      <td>2003-10-04 20:41:28</td>\n",
       "      <td>GT</td>\n",
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       "      <th>990</th>\n",
       "      <td>-0.5153</td>\n",
       "      <td>57.690300</td>\n",
       "      <td>2003-10-05 02:31:28</td>\n",
       "      <td>GT</td>\n",
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       "     Longitude   Latitude                Time Platform\n",
       "986     0.5003  57.240501 2003-10-03 22:24:21       GT\n",
       "987     0.4992  56.780300 2003-10-04 02:38:20       GT\n",
       "988    -0.3618  56.744801 2003-10-04 10:01:20       GT\n",
       "989    -0.5513  57.132500 2003-10-04 20:41:28       GT\n",
       "990    -0.5153  57.690300 2003-10-05 02:31:28       GT"
      ]
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     "output_type": "stream",
     "text": [
      "Platform HJ: 179 measurement locations.\n"
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       "      <th>174</th>\n",
       "      <td>12.2738</td>\n",
       "      <td>67.741798</td>\n",
       "      <td>2003-11-13 22:24:18</td>\n",
       "      <td>HJ</td>\n",
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       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>12.6193</td>\n",
       "      <td>67.946503</td>\n",
       "      <td>2003-11-14 00:25:53</td>\n",
       "      <td>HJ</td>\n",
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       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>10.4858</td>\n",
       "      <td>68.057503</td>\n",
       "      <td>2003-11-14 06:03:13</td>\n",
       "      <td>HJ</td>\n",
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       "      <th>177</th>\n",
       "      <td>11.0522</td>\n",
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       "      <td>2003-11-14 09:56:47</td>\n",
       "      <td>HJ</td>\n",
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       "      <th>178</th>\n",
       "      <td>12.4153</td>\n",
       "      <td>68.231300</td>\n",
       "      <td>2003-11-14 15:25:51</td>\n",
       "      <td>HJ</td>\n",
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       "     Longitude   Latitude                Time Platform\n",
       "174    12.2738  67.741798 2003-11-13 22:24:18       HJ\n",
       "175    12.6193  67.946503 2003-11-14 00:25:53       HJ\n",
       "176    10.4858  68.057503 2003-11-14 06:03:13       HJ\n",
       "177    11.0522  68.175697 2003-11-14 09:56:47       HJ\n",
       "178    12.4153  68.231300 2003-11-14 15:25:51       HJ"
      ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Platform JH: 950 measurement locations.\n"
     ]
    },
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       "      <th>945</th>\n",
       "      <td>15.6965</td>\n",
       "      <td>71.253998</td>\n",
       "      <td>2003-12-17 11:01:51</td>\n",
       "      <td>JH</td>\n",
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       "      <th>946</th>\n",
       "      <td>12.5057</td>\n",
       "      <td>71.252701</td>\n",
       "      <td>2003-12-17 22:14:57</td>\n",
       "      <td>JH</td>\n",
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       "      <th>947</th>\n",
       "      <td>13.0053</td>\n",
       "      <td>71.751503</td>\n",
       "      <td>2003-12-18 03:03:55</td>\n",
       "      <td>JH</td>\n",
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       "      <th>948</th>\n",
       "      <td>14.9853</td>\n",
       "      <td>71.743301</td>\n",
       "      <td>2003-12-18 07:15:07</td>\n",
       "      <td>JH</td>\n",
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       "      <th>949</th>\n",
       "      <td>17.0693</td>\n",
       "      <td>71.751701</td>\n",
       "      <td>2003-12-18 11:53:00</td>\n",
       "      <td>JH</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Longitude   Latitude                Time Platform\n",
       "945    15.6965  71.253998 2003-12-17 11:01:51       JH\n",
       "946    12.5057  71.252701 2003-12-17 22:14:57       JH\n",
       "947    13.0053  71.751503 2003-12-18 03:03:55       JH\n",
       "948    14.9853  71.743301 2003-12-18 07:15:07       JH\n",
       "949    17.0693  71.751701 2003-12-18 11:53:00       JH"
      ]
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Merged dataframe with all platforms. Total of 3209 measurement locations\n"
     ]
    },
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Time</th>\n",
       "      <th>Platform</th>\n",
       "      <th>Index_ABS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Index_Relative</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>0</th>\n",
       "      <td>4.6155</td>\n",
       "      <td>60.755299</td>\n",
       "      <td>2003-01-07 05:25:57</td>\n",
       "      <td>AA</td>\n",
       "      <td>0</td>\n",
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       "      <td>4.4483</td>\n",
       "      <td>60.748299</td>\n",
       "      <td>2003-01-07 06:05:08</td>\n",
       "      <td>AA</td>\n",
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       "      <th>2</th>\n",
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       "      <td>60.751499</td>\n",
       "      <td>2003-01-07 08:34:00</td>\n",
       "      <td>AA</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.1170</td>\n",
       "      <td>60.747799</td>\n",
       "      <td>2003-01-07 09:18:06</td>\n",
       "      <td>AA</td>\n",
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       "      <th>4</th>\n",
       "      <td>3.9475</td>\n",
       "      <td>60.752201</td>\n",
       "      <td>2003-01-07 09:59:37</td>\n",
       "      <td>AA</td>\n",
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       "    <tr>\n",
       "      <th>945</th>\n",
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       "      <td>71.253998</td>\n",
       "      <td>2003-12-17 11:01:51</td>\n",
       "      <td>JH</td>\n",
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       "      <td>12.5057</td>\n",
       "      <td>71.252701</td>\n",
       "      <td>2003-12-17 22:14:57</td>\n",
       "      <td>JH</td>\n",
       "      <td>3205</td>\n",
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       "      <td>13.0053</td>\n",
       "      <td>71.751503</td>\n",
       "      <td>2003-12-18 03:03:55</td>\n",
       "      <td>JH</td>\n",
       "      <td>3206</td>\n",
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       "    <tr>\n",
       "      <th>948</th>\n",
       "      <td>14.9853</td>\n",
       "      <td>71.743301</td>\n",
       "      <td>2003-12-18 07:15:07</td>\n",
       "      <td>JH</td>\n",
       "      <td>3207</td>\n",
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       "    <tr>\n",
       "      <th>949</th>\n",
       "      <td>17.0693</td>\n",
       "      <td>71.751701</td>\n",
       "      <td>2003-12-18 11:53:00</td>\n",
       "      <td>JH</td>\n",
       "      <td>3208</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>3209 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Longitude   Latitude                Time Platform  Index_ABS\n",
       "Index_Relative                                                              \n",
       "0                  4.6155  60.755299 2003-01-07 05:25:57       AA          0\n",
       "1                  4.4483  60.748299 2003-01-07 06:05:08       AA          1\n",
       "2                  4.2853  60.751499 2003-01-07 08:34:00       AA          2\n",
       "3                  4.1170  60.747799 2003-01-07 09:18:06       AA          3\n",
       "4                  3.9475  60.752201 2003-01-07 09:59:37       AA          4\n",
       "...                   ...        ...                 ...      ...        ...\n",
       "945               15.6965  71.253998 2003-12-17 11:01:51       JH       3204\n",
       "946               12.5057  71.252701 2003-12-17 22:14:57       JH       3205\n",
       "947               13.0053  71.751503 2003-12-18 03:03:55       JH       3206\n",
       "948               14.9853  71.743301 2003-12-18 07:15:07       JH       3207\n",
       "949               17.0693  71.751701 2003-12-18 11:53:00       JH       3208\n",
       "\n",
       "[3209 rows x 5 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load locations (long & lat) and TIME of ALL measurements\n",
    "from datetime import datetime, timedelta\n",
    "start_date = datetime(1950, 1, 1)\n",
    "    \n",
    "position_df = pd.DataFrame() # or gpd.GeoDataFrame()\n",
    "\n",
    "for key in position_dict.keys():\n",
    "    test = pd.DataFrame()\n",
    "    \n",
    "    test['Longitude'] = position_dict[key]['data']['LONGITUDE'].data.astype(float)\n",
    "    test['Latitude'] = position_dict[key]['data']['LATITUDE'].data.astype(float)\n",
    "    test['Time'] = position_dict[key]['data']['TIME'].data.astype(float)\n",
    "    test['Platform'] = key\n",
    "    \n",
    "    # Convert TIME from float to datetime\n",
    "    test['Time'] = [start_date + timedelta(t) for t in test.loc[:,'Time']]\n",
    "    length = len(test[test['Platform']==key])\n",
    "    print(f'Platform {key}: {length} measurement locations.')\n",
    "    display(test.tail())\n",
    "    \n",
    "    position_df = position_df.append(test) # or gpd.GeoDataFrame()\n",
    "    \n",
    "position_df['Index_ABS'] = np.arange(0,len(position_df))\n",
    "position_df = position_df.rename_axis(\"Index_Relative\")\n",
    "\n",
    "print(f'\\nMerged dataframe with all platforms. Total of {len(position_df)} measurement locations')\n",
    "position_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save dataframe to csv\n",
    "# position_df.to_csv('all_locations.csv', sep=',', header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot all Positions\n",
    "Plot locations on an interactive plot. \n",
    "\n",
    "First, define the dataframe that you would like to plot to the **df_toPlot** variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_toPlot = position_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hoover your mouse on a location to see longitude and latitude information. All locations are showed in blue, and only filtered locations are shown in red."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.3.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.3.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.3.2.min.js\"];\n",
       "  var css_urls = [];\n",
       "  \n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "    \n",
       "    \n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if (root.Bokeh !== undefined || force === true) {\n",
       "      \n",
       "    for (var i = 0; i < inline_js.length; i++) {\n",
       "      inline_js[i].call(root, root.Bokeh);\n",
       "    }\n",
       "    if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"1002\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"1002\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.3.2.min.js\": \"XypntL49z55iwGVUW4qsEu83zKL3XEcz0MjuGOQ9SlaaQ68X/g+k1FcioZi7oQAc\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.3.2.min.js\": \"bEsM86IHGDTLCS0Zod8a8WM6Y4+lafAL/eSiyQcuPzinmWNgNO2/olUF0Z2Dkn5i\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.3.2.min.js\": \"TX0gSQTdXTTeScqxj6PVQxTiRW8DOoGVwinyi1D3kxv7wuxQ02XkOxv0xwiypcAH\"};\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      if (url in hashes) {\n        element.crossOrigin = \"anonymous\";\n        element.integrity = \"sha384-\" + hashes[url];\n      }\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  \n  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.3.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.3.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.3.2.min.js\"];\n  var css_urls = [];\n  \n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    function(Bokeh) {\n    \n    \n    }\n  ];\n\n  function run_inline_js() {\n    \n    if (root.Bokeh !== undefined || force === true) {\n      \n    for (var i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n    if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"1002\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
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       "\n",
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       "  <div class=\"bk-root\" id=\"89b087e7-7875-4d62-afe8-c034b5ff4e49\" data-root-id=\"1004\"></div>\n"
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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[684]}},\"selected\":{\"id\":\"1038\"},\"selection_policy\":{\"id\":\"1039\"}},\"id\":\"1025\",\"type\":\"ColumnDataSource\"}],\"root_ids\":[\"1004\"]},\"title\":\"Bokeh Application\",\"version\":\"2.3.2\"}};\n",
       "  var render_items = [{\"docid\":\"d4af55c7-87ee-4138-b83e-1b965a9e6c23\",\"root_ids\":[\"1004\"],\"roots\":{\"1004\":\"89b087e7-7875-4d62-afe8-c034b5ff4e49\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1004"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh.plotting import figure, output_file, show, ColumnDataSource\n",
    "from bokeh.models import HoverTool\n",
    "from bokeh.io import output_notebook\n",
    "\n",
    "output_notebook() # necessary to show the plot \n",
    "\n",
    "hover = HoverTool(\n",
    "        tooltips=[\n",
    "            (\"index\", \"$index\"),\n",
    "            (\"(x,y)\", \"($x, $y)\"),\n",
    "            (\"Platform\", \"@Platform\"),            \n",
    "        ]\n",
    "    )\n",
    "\n",
    "p = figure(plot_width=500, plot_height=500, tools=[hover],\n",
    "           title=\"Mouse over the dots\")\n",
    "\n",
    "# p.square('Longitude', 'Latitude', size=6, color='grey', fill_color='white', source=df_toPlot, legend_label=\"All locations\")\n",
    "p.circle('Longitude', 'Latitude', size=4, color='red', fill_color='white', source=df_toPlot[df_toPlot['Platform']=='AA'], legend_label=\"Platform AA locations\")\n",
    "p.circle('Longitude', 'Latitude', size=4, color='blue', fill_color='white', source=df_toPlot[df_toPlot['Platform']=='GS'], legend_label=\"Platform GS locations\")\n",
    "p.circle('Longitude', 'Latitude', size=4, color='green', fill_color='white', source=df_toPlot[df_toPlot['Platform']=='GT'], legend_label=\"Platform GT locations\")\n",
    "p.circle('Longitude', 'Latitude', size=4, color='black', fill_color='white', source=df_toPlot[df_toPlot['Platform']=='HJ'], legend_label=\"Platform HJ locations\")\n",
    "p.circle('Longitude', 'Latitude', size=4, color='orange', fill_color='white', source=df_toPlot[df_toPlot['Platform']=='JH'], legend_label=\"Platform JH locations\")\n",
    "\n",
    "# Add area\n",
    "# p.quad(left=bbox[0], right=bbox[1], top=bbox[3], bottom=bbox[2], legend_label=\"Bounding Box\", fill_color='grey', fill_alpha=0.0, line_color=\"black\")\n",
    "\n",
    "p.legend.location = \"bottom_right\"\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the plotting above, some decisions could be made on bounding box of interest, platform, etc. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filter Positions (based on *position_df*)\n",
    "This section shows a few examples of data filtering by using the 'LATITUDE', 'LONGITUDE' and 'TIME' dimensions. The following filters are included:\n",
    "* filter by bounding box (BBOX)\n",
    "* filter by BBOX and platform's month of collection \n",
    "* filter by BBOX and platform's time (hour) of collection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filter by BBOX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now apply the arctic bounding box in the format: minLong - maxLong; minLat - maxLat (eg 30W - 60E; 50N - 80 N) \n",
    "bbox_dict = {}\n",
    "\n",
    "bbox_dict['BBOX=ALL'] = [-20, 70, 40, 90] \n",
    "bbox_dict['BBOX=Arctic'] = [-30, 60, 50, 80]  \n",
    "bbox_dict['BBOX=North Sea'] = [-5, 15, 50, 60]\n",
    "\n",
    "bbox_key = 'BBOX=North Sea'; assert bbox_key in bbox_dict.keys(), 'Key error, please check.'\n",
    "bbox = bbox_dict[bbox_key]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Year: 2003\n",
      "BBOX: BBOX=North Sea\n",
      "Selected positions (out of available positions): 564 out of 3209.\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Time</th>\n",
       "      <th>Platform</th>\n",
       "      <th>Index_ABS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Index_Relative</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-0.9848</td>\n",
       "      <td>59.284801</td>\n",
       "      <td>2003-01-12 19:55:32</td>\n",
       "      <td>AA</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-0.6563</td>\n",
       "      <td>59.284801</td>\n",
       "      <td>2003-01-12 21:10:15</td>\n",
       "      <td>AA</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-0.3618</td>\n",
       "      <td>59.284801</td>\n",
       "      <td>2003-01-12 22:13:23</td>\n",
       "      <td>AA</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.0065</td>\n",
       "      <td>59.281700</td>\n",
       "      <td>2003-01-12 23:30:54</td>\n",
       "      <td>AA</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.3293</td>\n",
       "      <td>59.284698</td>\n",
       "      <td>2003-01-13 00:56:57</td>\n",
       "      <td>AA</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986</th>\n",
       "      <td>0.5003</td>\n",
       "      <td>57.240501</td>\n",
       "      <td>2003-10-03 22:24:21</td>\n",
       "      <td>GT</td>\n",
       "      <td>2075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>0.4992</td>\n",
       "      <td>56.780300</td>\n",
       "      <td>2003-10-04 02:38:20</td>\n",
       "      <td>GT</td>\n",
       "      <td>2076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>-0.3618</td>\n",
       "      <td>56.744801</td>\n",
       "      <td>2003-10-04 10:01:20</td>\n",
       "      <td>GT</td>\n",
       "      <td>2077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>989</th>\n",
       "      <td>-0.5513</td>\n",
       "      <td>57.132500</td>\n",
       "      <td>2003-10-04 20:41:28</td>\n",
       "      <td>GT</td>\n",
       "      <td>2078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>990</th>\n",
       "      <td>-0.5153</td>\n",
       "      <td>57.690300</td>\n",
       "      <td>2003-10-05 02:31:28</td>\n",
       "      <td>GT</td>\n",
       "      <td>2079</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>564 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Longitude   Latitude                Time Platform  Index_ABS\n",
       "Index_Relative                                                              \n",
       "25                -0.9848  59.284801 2003-01-12 19:55:32       AA         25\n",
       "26                -0.6563  59.284801 2003-01-12 21:10:15       AA         26\n",
       "27                -0.3618  59.284801 2003-01-12 22:13:23       AA         27\n",
       "28                 0.0065  59.281700 2003-01-12 23:30:54       AA         28\n",
       "29                 0.3293  59.284698 2003-01-13 00:56:57       AA         29\n",
       "...                   ...        ...                 ...      ...        ...\n",
       "986                0.5003  57.240501 2003-10-03 22:24:21       GT       2075\n",
       "987                0.4992  56.780300 2003-10-04 02:38:20       GT       2076\n",
       "988               -0.3618  56.744801 2003-10-04 10:01:20       GT       2077\n",
       "989               -0.5513  57.132500 2003-10-04 20:41:28       GT       2078\n",
       "990               -0.5153  57.690300 2003-10-05 02:31:28       GT       2079\n",
       "\n",
       "[564 rows x 5 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now remove the ouliers outside bbox\n",
    "position_df_bbox = position_df[(position_df.loc[:,'Longitude'] >= bbox[0]) & \n",
    "                               (position_df.loc[:,'Longitude'] <= bbox[1]) & \n",
    "                               (position_df.loc[:,'Latitude'] >= bbox[2]) & \n",
    "                               (position_df.loc[:,'Latitude'] <= bbox[3])]\n",
    "print('Year:', year)\n",
    "print('BBOX:', bbox_key)\n",
    "\n",
    "sel_outof_all = f'{len(position_df_bbox)} out of {len(position_df)}.'\n",
    "print(f'Selected positions (out of available positions): {sel_outof_all}')\n",
    "display(position_df_bbox)\n",
    "#print(position_df_bbox.index) # You can use the display(data[var].data[:][position_df_bbox.index]) to select the physical variables values of only those points within specific bbox"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filter by BBOX and Month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected positions (out of available positions): 0 out of 3209.\n"
     ]
    },
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Time</th>\n",
       "      <th>Platform</th>\n",
       "      <th>Index_ABS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Index_Relative</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Longitude, Latitude, Time, Platform, Index_ABS]\n",
       "Index: []"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "month = '08'\n",
    "period = f'{year}-{month}'\n",
    "condition = (position_df_bbox['Time']).astype(str).str.contains(period)\n",
    "position_df_bbox_mm = position_df_bbox[condition]\n",
    "\n",
    "sel_outof_all = f'{len(position_df_bbox_mm)} out of {len(position_df)}.'\n",
    "print(f'Selected positions (out of available positions): {sel_outof_all}')\n",
    "\n",
    "position_df_bbox_mm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filter by BBOX and Hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected positions (out of available positions): 24 out of 3209.\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Time</th>\n",
       "      <th>Platform</th>\n",
       "      <th>Index_ABS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Index_Relative</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>8.1632</td>\n",
       "      <td>57.915798</td>\n",
       "      <td>2003-01-19 05:22:18</td>\n",
       "      <td>AA</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>6.3377</td>\n",
       "      <td>57.000198</td>\n",
       "      <td>2003-01-20 05:29:17</td>\n",
       "      <td>AA</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>4.6975</td>\n",
       "      <td>57.000702</td>\n",
       "      <td>2003-01-22 05:56:00</td>\n",
       "      <td>AA</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>-0.2325</td>\n",
       "      <td>56.999298</td>\n",
       "      <td>2003-01-24 05:14:13</td>\n",
       "      <td>AA</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>2.9035</td>\n",
       "      <td>59.282700</td>\n",
       "      <td>2003-03-07 05:06:37</td>\n",
       "      <td>AA</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>2.7532</td>\n",
       "      <td>59.282700</td>\n",
       "      <td>2003-03-07 05:45:36</td>\n",
       "      <td>AA</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>-2.0922</td>\n",
       "      <td>58.949699</td>\n",
       "      <td>2003-03-08 05:23:26</td>\n",
       "      <td>AA</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>3.3368</td>\n",
       "      <td>58.001202</td>\n",
       "      <td>2003-03-09 05:42:38</td>\n",
       "      <td>AA</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>7.2558</td>\n",
       "      <td>55.664200</td>\n",
       "      <td>2003-03-15 05:17:13</td>\n",
       "      <td>AA</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>4.5048</td>\n",
       "      <td>59.750702</td>\n",
       "      <td>2003-04-28 05:07:28</td>\n",
       "      <td>AA</td>\n",
       "      <td>384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>4.5002</td>\n",
       "      <td>57.750301</td>\n",
       "      <td>2003-04-30 05:01:18</td>\n",
       "      <td>AA</td>\n",
       "      <td>390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>462</th>\n",
       "      <td>3.5403</td>\n",
       "      <td>59.283001</td>\n",
       "      <td>2003-05-13 05:24:00</td>\n",
       "      <td>AA</td>\n",
       "      <td>462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>10.9758</td>\n",
       "      <td>58.054001</td>\n",
       "      <td>2003-11-05 05:00:00</td>\n",
       "      <td>AA</td>\n",
       "      <td>573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>7.0547</td>\n",
       "      <td>57.514000</td>\n",
       "      <td>2003-11-07 05:04:58</td>\n",
       "      <td>AA</td>\n",
       "      <td>594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>3.9982</td>\n",
       "      <td>59.499699</td>\n",
       "      <td>2003-10-18 05:10:23</td>\n",
       "      <td>GS</td>\n",
       "      <td>908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>0.1698</td>\n",
       "      <td>59.996700</td>\n",
       "      <td>2003-11-02 05:34:25</td>\n",
       "      <td>GS</td>\n",
       "      <td>994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>3.3620</td>\n",
       "      <td>59.285000</td>\n",
       "      <td>2003-11-04 05:31:55</td>\n",
       "      <td>GS</td>\n",
       "      <td>1019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>8.1980</td>\n",
       "      <td>57.849499</td>\n",
       "      <td>2003-07-03 05:21:15</td>\n",
       "      <td>GT</td>\n",
       "      <td>1713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676</th>\n",
       "      <td>3.0012</td>\n",
       "      <td>57.734798</td>\n",
       "      <td>2003-07-10 05:16:55</td>\n",
       "      <td>GT</td>\n",
       "      <td>1765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>722</th>\n",
       "      <td>-1.9300</td>\n",
       "      <td>59.283001</td>\n",
       "      <td>2003-07-16 05:49:39</td>\n",
       "      <td>GT</td>\n",
       "      <td>1811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>732</th>\n",
       "      <td>4.8192</td>\n",
       "      <td>59.898998</td>\n",
       "      <td>2003-07-18 05:19:40</td>\n",
       "      <td>GT</td>\n",
       "      <td>1821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947</th>\n",
       "      <td>3.1280</td>\n",
       "      <td>59.784698</td>\n",
       "      <td>2003-09-22 05:48:18</td>\n",
       "      <td>GT</td>\n",
       "      <td>2036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>966</th>\n",
       "      <td>3.4555</td>\n",
       "      <td>58.243999</td>\n",
       "      <td>2003-09-26 05:47:52</td>\n",
       "      <td>GT</td>\n",
       "      <td>2055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>4.5393</td>\n",
       "      <td>57.279800</td>\n",
       "      <td>2003-10-01 05:54:38</td>\n",
       "      <td>GT</td>\n",
       "      <td>2063</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Longitude   Latitude                Time Platform  Index_ABS\n",
       "Index_Relative                                                              \n",
       "55                 8.1632  57.915798 2003-01-19 05:22:18       AA         55\n",
       "70                 6.3377  57.000198 2003-01-20 05:29:17       AA         70\n",
       "80                 4.6975  57.000702 2003-01-22 05:56:00       AA         80\n",
       "99                -0.2325  56.999298 2003-01-24 05:14:13       AA         99\n",
       "141                2.9035  59.282700 2003-03-07 05:06:37       AA        141\n",
       "142                2.7532  59.282700 2003-03-07 05:45:36       AA        142\n",
       "159               -2.0922  58.949699 2003-03-08 05:23:26       AA        159\n",
       "170                3.3368  58.001202 2003-03-09 05:42:38       AA        170\n",
       "220                7.2558  55.664200 2003-03-15 05:17:13       AA        220\n",
       "384                4.5048  59.750702 2003-04-28 05:07:28       AA        384\n",
       "390                4.5002  57.750301 2003-04-30 05:01:18       AA        390\n",
       "462                3.5403  59.283001 2003-05-13 05:24:00       AA        462\n",
       "573               10.9758  58.054001 2003-11-05 05:00:00       AA        573\n",
       "594                7.0547  57.514000 2003-11-07 05:04:58       AA        594\n",
       "224                3.9982  59.499699 2003-10-18 05:10:23       GS        908\n",
       "310                0.1698  59.996700 2003-11-02 05:34:25       GS        994\n",
       "335                3.3620  59.285000 2003-11-04 05:31:55       GS       1019\n",
       "624                8.1980  57.849499 2003-07-03 05:21:15       GT       1713\n",
       "676                3.0012  57.734798 2003-07-10 05:16:55       GT       1765\n",
       "722               -1.9300  59.283001 2003-07-16 05:49:39       GT       1811\n",
       "732                4.8192  59.898998 2003-07-18 05:19:40       GT       1821\n",
       "947                3.1280  59.784698 2003-09-22 05:48:18       GT       2036\n",
       "966                3.4555  58.243999 2003-09-26 05:47:52       GT       2055\n",
       "974                4.5393  57.279800 2003-10-01 05:54:38       GT       2063"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hour = '05'\n",
    "hour_expr = f' {hour}:'\n",
    "condition = (position_df_bbox['Time']).astype(str).str.contains(hour_expr)\n",
    "position_df_bbox_hh = position_df_bbox[condition]\n",
    "\n",
    "sel_outof_all = f'{len(position_df_bbox_hh)} out of {len(position_df)}.'\n",
    "print(f'Selected positions (out of available positions): {sel_outof_all}')\n",
    "\n",
    "position_df_bbox_hh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filter by Time Range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time Filter: 2003-06-17 - 2003-06-29\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Time</th>\n",
       "      <th>Platform</th>\n",
       "      <th>Index_ABS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Index_Relative</th>\n",
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       "      <th>566</th>\n",
       "      <td>31.204000</td>\n",
       "      <td>70.748299</td>\n",
       "      <td>2003-06-17 00:41:26</td>\n",
       "      <td>GT</td>\n",
       "      <td>1655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>31.213301</td>\n",
       "      <td>71.000198</td>\n",
       "      <td>2003-06-17 03:12:43</td>\n",
       "      <td>GT</td>\n",
       "      <td>1656</td>\n",
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       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>31.216801</td>\n",
       "      <td>71.248497</td>\n",
       "      <td>2003-06-17 06:58:21</td>\n",
       "      <td>GT</td>\n",
       "      <td>1657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>31.208300</td>\n",
       "      <td>71.500999</td>\n",
       "      <td>2003-06-17 08:55:13</td>\n",
       "      <td>GT</td>\n",
       "      <td>1658</td>\n",
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       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>31.210501</td>\n",
       "      <td>71.748299</td>\n",
       "      <td>2003-06-17 11:41:23</td>\n",
       "      <td>GT</td>\n",
       "      <td>1659</td>\n",
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       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>-1.993000</td>\n",
       "      <td>69.002296</td>\n",
       "      <td>2003-06-27 06:11:18</td>\n",
       "      <td>JH</td>\n",
       "      <td>2766</td>\n",
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       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>-0.641500</td>\n",
       "      <td>68.250999</td>\n",
       "      <td>2003-06-27 15:28:11</td>\n",
       "      <td>JH</td>\n",
       "      <td>2767</td>\n",
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       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>0.733800</td>\n",
       "      <td>68.250999</td>\n",
       "      <td>2003-06-28 00:09:21</td>\n",
       "      <td>JH</td>\n",
       "      <td>2768</td>\n",
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       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>1.711800</td>\n",
       "      <td>68.263702</td>\n",
       "      <td>2003-06-28 08:47:51</td>\n",
       "      <td>JH</td>\n",
       "      <td>2769</td>\n",
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       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>5.608800</td>\n",
       "      <td>68.241699</td>\n",
       "      <td>2003-06-28 22:54:03</td>\n",
       "      <td>JH</td>\n",
       "      <td>2770</td>\n",
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       "<p>79 rows × 5 columns</p>\n",
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      ],
      "text/plain": [
       "                Longitude   Latitude                Time Platform  Index_ABS\n",
       "Index_Relative                                                              \n",
       "566             31.204000  70.748299 2003-06-17 00:41:26       GT       1655\n",
       "567             31.213301  71.000198 2003-06-17 03:12:43       GT       1656\n",
       "568             31.216801  71.248497 2003-06-17 06:58:21       GT       1657\n",
       "569             31.208300  71.500999 2003-06-17 08:55:13       GT       1658\n",
       "570             31.210501  71.748299 2003-06-17 11:41:23       GT       1659\n",
       "...                   ...        ...                 ...      ...        ...\n",
       "507             -1.993000  69.002296 2003-06-27 06:11:18       JH       2766\n",
       "508             -0.641500  68.250999 2003-06-27 15:28:11       JH       2767\n",
       "509              0.733800  68.250999 2003-06-28 00:09:21       JH       2768\n",
       "510              1.711800  68.263702 2003-06-28 08:47:51       JH       2769\n",
       "511              5.608800  68.241699 2003-06-28 22:54:03       JH       2770\n",
       "\n",
       "[79 rows x 5 columns]"
      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "# Define start and end date in the format [day,month,year]\n",
    "time_start = [17,6,2003]\n",
    "time_start = datetime(time_start[2], time_start[1], time_start[0])\n",
    "\n",
    "time_end = [29,6,2003]\n",
    "time_end = datetime(time_end[2], time_end[1], time_end[0])\n",
    "\n",
    "print(f'Time Filter: {time_start.strftime(\"%Y-%m-%d\")} - {time_end.strftime(\"%Y-%m-%d\")}')\n",
    "\n",
    "position_df_timerange = position_df.loc[(position_df['Time']>=time_start) & \n",
    "                                        (position_df['Time']<=time_end)]\n",
    "display(position_df_timerange)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create dictionary with list of filtered indices\n",
    "This enebles saving the indices of the filtered data into a dictionary, for each platform, which can then be saved, exported to csv file, and loaded to another notebook for further use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create dictionary with list of filtered indices, for each platform \n",
    "import pickle\n",
    "timerange_index = {}\n",
    "\n",
    "for key in position_df_timerange['Platform'].unique():\n",
    "    sel = position_df_timerange.loc[position_df_timerange['Platform']==key]\n",
    "    timerange_index[key] = sel.index.tolist()\n",
    "\n",
    "# pprint.pprint(timerange_index)\n",
    "\n",
    "# # Save dictionary to file\n",
    "# myfile = open(\"timerange_index.pkl\", \"wb\")\n",
    "# pickle.dump(timerange_index, myfile)\n",
    "# myfile.close()\n",
    "\n",
    "# # Load dictionary from file\n",
    "# myfile = open(\"timerange_index.pkl\", \"rb\")\n",
    "# timerange_index = pickle.load(myfile)\n",
    "# myfile.close()\n",
    "# print(timerange_index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**OUTPUT**: a list of the indices that have been filtered, which can be exported to csv or saved to a dictionary for further use."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Processing: Load and Plot all Data (Variables and their Attributes)\n",
    "\n",
    "This section goes deeper into the data analysis by accessing all data (variables and their attributes). This is therefore more computationally-demanding, especially if multiple datasets of many measurements are analysed at once. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": "true"
   },
   "source": [
    "## Fetch Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create **data_dict** dictionary \n",
    "All data (variables and their attributes) for each platform are read iteratively, and saved into a dictionary *data_dict* which contains:\n",
    "* the actual data, loaded into an **xarray** for data handling, analysis and visualisation\n",
    "* the campaign's main attributes: platform code & name, data type, title, instrument, longitude & latitude, and vertical min & max)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://opendap.terradue.com/hyrax/data/subset_2003/58AA_CTD_2003.nc.nc4\n",
      "['58AA' 'H\\\\\\\\303\\\\\\\\245kon Mosby' '2003' 'OceanSITES vertical profile'\n",
      " 'Arctic Ocean - In Situ Observation Copernicus' 'CTD Seabird' '0.000'\n",
      " '2811.000' '-11.884' '17.833' '53.996' '69.999']\n",
      "\n",
      "https://opendap.terradue.com/hyrax/data/subset_2003/58GS_CTD_2003.nc.nc4\n",
      "['58GS' 'G.O. Sars' '2003' 'OceanSITES vertical profile'\n",
      " 'Arctic Ocean - In Situ Observation Copernicus' 'CTD Seabird' '1.000'\n",
      " '3688.000' '-17.979' '22.532' '59.28' '78.336']\n",
      "\n",
      "https://opendap.terradue.com/hyrax/data/subset_2003/58GT_CTD_2003.nc.nc4\n",
      "['58GT' 'Sarsen' '2003' 'OceanSITES vertical profile'\n",
      " 'Arctic Ocean - In Situ Observation Copernicus' 'CTD Seabird' '1.000'\n",
      " '2962.000' '-9.52' '35.016' '56.667' '78.655']\n",
      "\n",
      "https://opendap.terradue.com/hyrax/data/subset_2003/58HJ_CTD_2003.nc.nc4\n",
      "['58HJ' 'Helmer Hanssen' '2003' 'OceanSITES vertical profile'\n",
      " 'Arctic Ocean - In Situ Observation Copernicus' 'CTD Seabird' '1.000'\n",
      " '792.000' '6.303' '39.826' '63.889' '81.179']\n",
      "\n",
      "https://opendap.terradue.com/hyrax/data/subset_2003/58JH_CTD_2003.nc.nc4\n",
      "['58JH' 'Johan Hjort' '2003' 'OceanSITES vertical profile'\n",
      " 'Arctic Ocean - In Situ Observation Copernicus' 'CTD Seabird' '1.000'\n",
      " '3766.000' '-15.502' '47.017' '50.998' '77.829']\n",
      "\n",
      "Checking the existing campaigns in the dictionary: ['AA', 'GS', 'GT', 'HJ', 'JH']\n"
     ]
    }
   ],
   "source": [
    "data_dict = {}\n",
    "\n",
    "for pc in platform_codes:\n",
    "    \n",
    "    fix_lab = f'58{pc}_CTD_{year}' # platform_codes and year are defined at the beginning of the notebook \n",
    "\n",
    "    url = server_url + fix_lab + '.nc.nc4'; print(url)\n",
    "    \n",
    "    remote_data, data_attr = fetch_data(url)\n",
    "    \n",
    "    data_dict[pc] = {'data': remote_data, \n",
    "                     'data_attr': data_attr}\n",
    "    \n",
    "    print(f'{data_attr}\\n')\n",
    "\n",
    "# display(data_dict)\n",
    "print(f'Checking the existing campaigns in the dictionary: {list(data_dict.keys())}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
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       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (DEPTH: 2957, LATITUDE: 991, LONGITUDE: 991, POSITION: 991, TIME: 991)\n",
       "Coordinates:\n",
       "  * TIME         (TIME) float64 1.938e+04 1.938e+04 ... 1.963e+04 1.964e+04\n",
       "  * LATITUDE     (LATITUDE) float32 62.37 62.49 62.6 62.72 ... 56.74 57.13 57.69\n",
       "  * LONGITUDE    (LONGITUDE) float32 5.2 4.948 4.7 ... -0.3618 -0.5513 -0.5153\n",
       "Dimensions without coordinates: DEPTH, POSITION\n",
       "Data variables:\n",
       "    TIME_QC      (TIME) float32 1.0 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0 1.0\n",
       "    POSITION_QC  (POSITION) float32 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0 1.0\n",
       "    DIRECTION    object &#x27;DDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDD...\n",
       "    PRES         (TIME, DEPTH) float32 ...\n",
       "    PRES_QC      (TIME, DEPTH) float32 ...\n",
       "    TEMP         (TIME, DEPTH) float64 ...\n",
       "    PSAL         (TIME, DEPTH) float64 ...\n",
       "    CNDC         (TIME, DEPTH) float64 ...\n",
       "    TEMP_QC      (TIME, DEPTH) float32 ...\n",
       "    PSAL_QC      (TIME, DEPTH) float32 ...\n",
       "    CNDC_QC      (TIME, DEPTH) float32 ...\n",
       "Attributes: (12/51)\n",
       "    title:                          Arctic Ocean - In Situ Observation Copern...\n",
       "    qc_manual:                      Recommendations for in-situ data Near Rea...\n",
       "    contact:                        cmems-service@imr.no\n",
       "    format_version:                 1.4\n",
       "    distribution_statement:         These data follow Copernicus standards; t...\n",
       "    citation:                       These data were collected and made freely...\n",
       "    ...                             ...\n",
       "    last_latitude_observation:      57.690\n",
       "    geospatial_lon_min:             -9.520\n",
       "    geospatial_lon_max:             35.016\n",
       "    last_longitude_observation:     -0.515\n",
       "    geospatial_vertical_min:        1.000\n",
       "    geospatial_vertical_max:        2962.000</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-2da57870-1aec-450a-8063-a23283c3525b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2da57870-1aec-450a-8063-a23283c3525b' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>DEPTH</span>: 2957</li><li><span class='xr-has-index'>LATITUDE</span>: 991</li><li><span class='xr-has-index'>LONGITUDE</span>: 991</li><li><span>POSITION</span>: 991</li><li><span class='xr-has-index'>TIME</span>: 991</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-bdb6f9a3-6b41-43d9-909c-d48f0171229f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bdb6f9a3-6b41-43d9-909c-d48f0171229f' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>TIME</span></div><div class='xr-var-dims'>(TIME)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.938e+04 1.938e+04 ... 1.964e+04</div><input id='attrs-f4620a47-cf94-4753-b460-bc56b2823310' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4620a47-cf94-4753-b460-bc56b2823310' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b04fda28-ef02-4dbd-b80a-13be71625364' class='xr-var-data-in' type='checkbox'><label for='data-b04fda28-ef02-4dbd-b80a-13be71625364' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>units :</span></dt><dd>days since 1950-01-01T00:00:00Z</dd><dt><span>valid_min :</span></dt><dd>-90000.0</dd><dt><span>valid_max :</span></dt><dd>90000.0</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>QC_indicator :</span></dt><dd>1</dd><dt><span>uncertainty :</span></dt><dd></dd><dt><span>comment :</span></dt><dd></dd><dt><span>ancillary_variables :</span></dt><dd>TIME_QC</dd><dt><span>calendar :</span></dt><dd>standard</dd></dl></div><div class='xr-var-data'><pre>array([19376.416007, 19376.465451, 19376.541806, ..., 19634.417593,\n",
       "       19634.86213 , 19635.105185])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>LATITUDE</span></div><div class='xr-var-dims'>(LATITUDE)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>62.37 62.49 62.6 ... 57.13 57.69</div><input id='attrs-39de9078-9f10-43ff-8a72-829aaca19426' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-39de9078-9f10-43ff-8a72-829aaca19426' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b753d00-5c55-4cf1-900d-d934a146402e' class='xr-var-data-in' type='checkbox'><label for='data-5b753d00-5c55-4cf1-900d-d934a146402e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>QC_indicator :</span></dt><dd>1</dd><dt><span>uncertainty :</span></dt><dd> </dd><dt><span>comment :</span></dt><dd> </dd><dt><span>ancillary_variables :</span></dt><dd>POSITION_QC</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude of each location</dd><dt><span>units :</span></dt><dd>degree_north</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd></dl></div><div class='xr-var-data'><pre>array([62.3652, 62.486 , 62.6048, ..., 56.7448, 57.1325, 57.6903],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>LONGITUDE</span></div><div class='xr-var-dims'>(LONGITUDE)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>5.2 4.948 4.7 ... -0.5513 -0.5153</div><input id='attrs-a7e813c1-c711-4359-9359-ee378ec32aa3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a7e813c1-c711-4359-9359-ee378ec32aa3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a57f2c7-1e9a-4414-8bd0-75bf073a016b' class='xr-var-data-in' type='checkbox'><label for='data-8a57f2c7-1e9a-4414-8bd0-75bf073a016b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>QC_indicator :</span></dt><dd>1</dd><dt><span>uncertainty :</span></dt><dd> </dd><dt><span>comment :</span></dt><dd> </dd><dt><span>ancillary_variables :</span></dt><dd>POSITION_QC</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude of each location</dd><dt><span>units :</span></dt><dd>degree_east</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd></dl></div><div class='xr-var-data'><pre>array([ 5.1997,  4.9483,  4.7002, ..., -0.3618, -0.5513, -0.5153],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bad0cc97-81ad-4ad2-b610-b52e28449d11' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bad0cc97-81ad-4ad2-b610-b52e28449d11' class='xr-section-summary' >Data variables: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>TIME_QC</span></div><div class='xr-var-dims'>(TIME)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e346d343-810b-460f-9e78-6ca2dc345f51' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e346d343-810b-460f-9e78-6ca2dc345f51' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c22cbcbd-37b0-4907-9272-dfbcb4809380' class='xr-var-data-in' type='checkbox'><label for='data-c22cbcbd-37b0-4907-9272-dfbcb4809380' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>conventions :</span></dt><dd>Copernicus Marine In Situ reference table 2</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>9</dd><dt><span>flag_values :</span></dt><dd>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]</dd><dt><span>flag_meanings :</span></dt><dd>no_qc_performed good_data probably_good_data bad_data_that_are_potentially_correctable bad_data value_changed not_used nominal_value interpolated_value missing_value</dd><dt><span>long_name :</span></dt><dd>Time quality flag</dd></dl></div><div class='xr-var-data'><pre>array([1., 1., 1., ..., 1., 1., 1.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>POSITION_QC</span></div><div class='xr-var-dims'>(POSITION)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-eb86a4af-02c6-41d3-babb-01ff8d893fd1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eb86a4af-02c6-41d3-babb-01ff8d893fd1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f96b56d-da72-4ce2-8c24-8e8971ac80a0' class='xr-var-data-in' type='checkbox'><label for='data-6f96b56d-da72-4ce2-8c24-8e8971ac80a0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>conventions :</span></dt><dd>Copernicus Marine In Situ reference table 2</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>9</dd><dt><span>flag_values :</span></dt><dd>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]</dd><dt><span>flag_meanings :</span></dt><dd>no_qc_performed good_data probably_good_data bad_data_that_are_potentially_correctable bad_data value_changed not_used nominal_value interpolated_value missing_value</dd><dt><span>long_name :</span></dt><dd>Position quality flag</dd></dl></div><div class='xr-var-data'><pre>array([1., 1., 1., ..., 1., 1., 1.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DIRECTION</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6c038086-9e32-4b68-ba82-e12aeeeccd2f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c038086-9e32-4b68-ba82-e12aeeeccd2f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-54db60e7-6436-42f2-b601-f6cacda3d6a3' class='xr-var-data-in' type='checkbox'><label for='data-54db60e7-6436-42f2-b601-f6cacda3d6a3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Direction of the profiles</dd><dt><span>conventions :</span></dt><dd>A: ascending profile, D: descending profile</dd><dt><span>string_length :</span></dt><dd>[991, 992]</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;DDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDD&#x27;,\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PRES</span></div><div class='xr-var-dims'>(TIME, DEPTH)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9eeb3542-0045-40f4-a4d1-9e039c17ad29' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9eeb3542-0045-40f4-a4d1-9e039c17ad29' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e055e2cc-e3ee-45a7-af53-23759e158ee1' class='xr-var-data-in' type='checkbox'><label for='data-e055e2cc-e3ee-45a7-af53-23759e158ee1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>uncertainty :</span></dt><dd> </dd><dt><span>comment :</span></dt><dd> </dd><dt><span>ancillary_variables :</span></dt><dd>PRES_QC</dd><dt><span>standard_name :</span></dt><dd>sea_water_pressure</dd><dt><span>long_name :</span></dt><dd>Sea pressure</dd><dt><span>units :</span></dt><dd>dbar</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>data_mode :</span></dt><dd>D</dd></dl></div><div class='xr-var-data'><pre>[2930387 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PRES_QC</span></div><div class='xr-var-dims'>(TIME, DEPTH)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-774e686b-2580-4b05-804b-f42ca8488e53' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-774e686b-2580-4b05-804b-f42ca8488e53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10755f84-fa3e-406b-b8d9-fdac5ea16eb9' class='xr-var-data-in' type='checkbox'><label for='data-10755f84-fa3e-406b-b8d9-fdac5ea16eb9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>conventions :</span></dt><dd>Copernicus Marine In Situ reference table 2</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>9</dd><dt><span>flag_values :</span></dt><dd>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]</dd><dt><span>flag_meanings :</span></dt><dd>no_qc_performed good_data probably_good_data bad_data_that_are_potentially_correctable bad_data value_changed not_used nominal_value interpolated_value missing_value</dd><dt><span>long_name :</span></dt><dd>Sea pressure quality flag</dd></dl></div><div class='xr-var-data'><pre>[2930387 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TEMP</span></div><div class='xr-var-dims'>(TIME, DEPTH)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9f6d4371-567e-45a0-88a0-21a3c2888a52' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9f6d4371-567e-45a0-88a0-21a3c2888a52' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce2e90dd-3c79-4777-b27a-b6cd6664e704' class='xr-var-data-in' type='checkbox'><label for='data-ce2e90dd-3c79-4777-b27a-b6cd6664e704' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>sea_water_temperature</dd><dt><span>long_name :</span></dt><dd>Sea temperature</dd><dt><span>units :</span></dt><dd>degrees_C</dd><dt><span>valid_min :</span></dt><dd>-2500</dd><dt><span>valid_max :</span></dt><dd>40000</dd><dt><span>ancillary_variables :</span></dt><dd>TEMP_QC TEMP_DM</dd><dt><span>data_mode :</span></dt><dd>D</dd></dl></div><div class='xr-var-data'><pre>[2930387 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PSAL</span></div><div class='xr-var-dims'>(TIME, DEPTH)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3e95b1ad-7e55-4002-8d8a-89e85998dbd9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3e95b1ad-7e55-4002-8d8a-89e85998dbd9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ad547a5-7723-4e65-844a-ee148c13abf1' class='xr-var-data-in' type='checkbox'><label for='data-2ad547a5-7723-4e65-844a-ee148c13abf1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>sea_water_practical_salinity</dd><dt><span>long_name :</span></dt><dd>Practical salinity</dd><dt><span>units :</span></dt><dd>0.001</dd><dt><span>valid_min :</span></dt><dd>2000</dd><dt><span>valid_max :</span></dt><dd>41000</dd><dt><span>ancillary_variables :</span></dt><dd>PSAL_QC PSAL_DM</dd><dt><span>data_mode :</span></dt><dd>D</dd></dl></div><div class='xr-var-data'><pre>[2930387 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CNDC</span></div><div class='xr-var-dims'>(TIME, DEPTH)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2a2c2b21-537c-46ce-99ea-4ce2f72a753d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2a2c2b21-537c-46ce-99ea-4ce2f72a753d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c1902ec-e368-4e8d-b4d3-ae9c8c1b4db6' class='xr-var-data-in' type='checkbox'><label for='data-1c1902ec-e368-4e8d-b4d3-ae9c8c1b4db6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>sea_water_electrical_conductivity</dd><dt><span>long_name :</span></dt><dd>Electrical conductivity</dd><dt><span>units :</span></dt><dd>S m-1</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>50000</dd><dt><span>ancillary_variables :</span></dt><dd>CNDC_QC CNDC_DM</dd><dt><span>data_mode :</span></dt><dd>D</dd></dl></div><div class='xr-var-data'><pre>[2930387 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TEMP_QC</span></div><div class='xr-var-dims'>(TIME, DEPTH)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-334fd3dd-cdbb-4e60-ba75-e8776feb7c6b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-334fd3dd-cdbb-4e60-ba75-e8776feb7c6b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23ccf55f-1d63-496f-96f3-a956375a920e' class='xr-var-data-in' type='checkbox'><label for='data-23ccf55f-1d63-496f-96f3-a956375a920e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>conventions :</span></dt><dd>Copernicus Marine In Situ reference table 2</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>9</dd><dt><span>flag_values :</span></dt><dd>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]</dd><dt><span>flag_meanings :</span></dt><dd>no_qc_performed good_data probably_good_data bad_data_that_are_potentially_correctable bad_data value_changed not_used nominal_value interpolated_value missing_value</dd><dt><span>long_name :</span></dt><dd>Sea temperature quality flag</dd></dl></div><div class='xr-var-data'><pre>[2930387 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PSAL_QC</span></div><div class='xr-var-dims'>(TIME, DEPTH)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6c1f140e-4a8f-4ee2-9834-bf68f3ce170e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c1f140e-4a8f-4ee2-9834-bf68f3ce170e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55df2fa3-9ea4-4172-8ae1-e125466e1845' class='xr-var-data-in' type='checkbox'><label for='data-55df2fa3-9ea4-4172-8ae1-e125466e1845' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>conventions :</span></dt><dd>Copernicus Marine In Situ reference table 2</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>9</dd><dt><span>flag_values :</span></dt><dd>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]</dd><dt><span>flag_meanings :</span></dt><dd>no_qc_performed good_data probably_good_data bad_data_that_are_potentially_correctable bad_data value_changed not_used nominal_value interpolated_value missing_value</dd><dt><span>long_name :</span></dt><dd>Practical salinity quality flag</dd></dl></div><div class='xr-var-data'><pre>[2930387 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CNDC_QC</span></div><div class='xr-var-dims'>(TIME, DEPTH)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-aa566631-57e0-428d-a12b-d78558ec0993' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aa566631-57e0-428d-a12b-d78558ec0993' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de695d19-61fb-4d1b-aa9c-86f5ef79389f' class='xr-var-data-in' type='checkbox'><label for='data-de695d19-61fb-4d1b-aa9c-86f5ef79389f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>conventions :</span></dt><dd>Copernicus Marine In Situ reference table 2</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>9</dd><dt><span>flag_values :</span></dt><dd>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]</dd><dt><span>flag_meanings :</span></dt><dd>no_qc_performed good_data probably_good_data bad_data_that_are_potentially_correctable bad_data value_changed not_used nominal_value interpolated_value missing_value</dd><dt><span>long_name :</span></dt><dd>Electrical conductivity quality flag</dd></dl></div><div class='xr-var-data'><pre>[2930387 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f75835b6-9618-45a7-9d4c-35a18d396b1c' class='xr-section-summary-in' type='checkbox'  ><label for='section-f75835b6-9618-45a7-9d4c-35a18d396b1c' class='xr-section-summary' >Attributes: <span>(51)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>Arctic Ocean - In Situ Observation Copernicus</dd><dt><span>qc_manual :</span></dt><dd>Recommendations for in-situ data Near Real Time Quality Control https://doi.org/10.13155/36230</dd><dt><span>contact :</span></dt><dd>cmems-service@imr.no</dd><dt><span>format_version :</span></dt><dd>1.4</dd><dt><span>distribution_statement :</span></dt><dd>These data follow Copernicus standards; they are public and free of charge. User assumes all risk for use of data. User must display citation in any publication or product using data. User must contact PI prior to any commercial use of data.</dd><dt><span>citation :</span></dt><dd>These data were collected and made freely available by the Copernicus project and the programs that contribute to it </dd><dt><span>naming_authority :</span></dt><dd>Copernicus Marine In Situ</dd><dt><span>data_assembly_center :</span></dt><dd>IMR</dd><dt><span>update_interval :</span></dt><dd>void</dd><dt><span>area :</span></dt><dd>Arctic Ocean</dd><dt><span>author :</span></dt><dd></dd><dt><span>Conventions :</span></dt><dd>CF-1.6 Copernicus-InSituTAC-FormatManual-1.41 Copernicus-InSituTAC-SRD-1.5 Copernicus-InSituTAC-ParametersList-3.2.0</dd><dt><span>data_mode :</span></dt><dd>D</dd><dt><span>comment :</span></dt><dd></dd><dt><span>history :</span></dt><dd>\\0122021-04-12 07:22:38 GMT Hyrax-1.14.0 http://opendap1.nodc.no:80/opendap/physics/point/yearly/58GT_CTD_2003.nc.nc4?</dd><dt><span>references :</span></dt><dd>http://marine.copernicus.eu http://www.marineinsitu.eu</dd><dt><span>netcdf_version :</span></dt><dd>netCDF-4 classic model</dd><dt><span>quality_index :</span></dt><dd>A</dd><dt><span>quality_control_indicator :</span></dt><dd>0</dd><dt><span>doi :</span></dt><dd></dd><dt><span>summary :</span></dt><dd></dd><dt><span>wmo_platform_code :</span></dt><dd></dd><dt><span>ices_platform_code :</span></dt><dd></dd><dt><span>site_code :</span></dt><dd></dd><dt><span>bottom_depth :</span></dt><dd></dd><dt><span>wmo_inst_type :</span></dt><dd></dd><dt><span>platform_code :</span></dt><dd>58GT</dd><dt><span>platform_name :</span></dt><dd>Sarsen</dd><dt><span>institution :</span></dt><dd>Institute of Marine Research (Norway)</dd><dt><span>institution_edmo_code :</span></dt><dd>1351</dd><dt><span>source :</span></dt><dd>research vessel</dd><dt><span>institution_references :</span></dt><dd>http://www.imr.no</dd><dt><span>institution_country :</span></dt><dd>Norway</dd><dt><span>source_platform_category_code :</span></dt><dd>31</dd><dt><span>pi_name :</span></dt><dd></dd><dt><span>data_type :</span></dt><dd>OceanSITES vertical profile</dd><dt><span>cdm_data_type :</span></dt><dd>profile</dd><dt><span>instrument :</span></dt><dd>CTD Seabird</dd><dt><span>id :</span></dt><dd>58GT_CTD_2003</dd><dt><span>time_coverage_start :</span></dt><dd>2003-01-19T09:59:03Z</dd><dt><span>time_coverage_end :</span></dt><dd>2003-10-05T02:31:28Z</dd><dt><span>last_date_observation :</span></dt><dd>2003-10-05T02:31:28Z</dd><dt><span>date_update :</span></dt><dd>2021-03-23T08:36:42Z</dd><dt><span>geospatial_lat_min :</span></dt><dd>56.667</dd><dt><span>geospatial_lat_max :</span></dt><dd>78.655</dd><dt><span>last_latitude_observation :</span></dt><dd>57.690</dd><dt><span>geospatial_lon_min :</span></dt><dd>-9.520</dd><dt><span>geospatial_lon_max :</span></dt><dd>35.016</dd><dt><span>last_longitude_observation :</span></dt><dd>-0.515</dd><dt><span>geospatial_vertical_min :</span></dt><dd>1.000</dd><dt><span>geospatial_vertical_max :</span></dt><dd>2962.000</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:      (DEPTH: 2957, LATITUDE: 991, LONGITUDE: 991, POSITION: 991, TIME: 991)\n",
       "Coordinates:\n",
       "  * TIME         (TIME) float64 1.938e+04 1.938e+04 ... 1.963e+04 1.964e+04\n",
       "  * LATITUDE     (LATITUDE) float32 62.37 62.49 62.6 62.72 ... 56.74 57.13 57.69\n",
       "  * LONGITUDE    (LONGITUDE) float32 5.2 4.948 4.7 ... -0.3618 -0.5513 -0.5153\n",
       "Dimensions without coordinates: DEPTH, POSITION\n",
       "Data variables:\n",
       "    TIME_QC      (TIME) float32 ...\n",
       "    POSITION_QC  (POSITION) float32 ...\n",
       "    DIRECTION    object ...\n",
       "    PRES         (TIME, DEPTH) float32 ...\n",
       "    PRES_QC      (TIME, DEPTH) float32 ...\n",
       "    TEMP         (TIME, DEPTH) float64 ...\n",
       "    PSAL         (TIME, DEPTH) float64 ...\n",
       "    CNDC         (TIME, DEPTH) float64 ...\n",
       "    TEMP_QC      (TIME, DEPTH) float32 ...\n",
       "    PSAL_QC      (TIME, DEPTH) float32 ...\n",
       "    CNDC_QC      (TIME, DEPTH) float32 ...\n",
       "Attributes: (12/51)\n",
       "    title:                          Arctic Ocean - In Situ Observation Copern...\n",
       "    qc_manual:                      Recommendations for in-situ data Near Rea...\n",
       "    contact:                        cmems-service@imr.no\n",
       "    format_version:                 1.4\n",
       "    distribution_statement:         These data follow Copernicus standards; t...\n",
       "    citation:                       These data were collected and made freely...\n",
       "    ...                             ...\n",
       "    last_latitude_observation:      57.690\n",
       "    geospatial_lon_min:             -9.520\n",
       "    geospatial_lon_max:             35.016\n",
       "    last_longitude_observation:     -0.515\n",
       "    geospatial_vertical_min:        1.000\n",
       "    geospatial_vertical_max:        2962.000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show xarray.Dataset of one platform as example\n",
    "data_dict['GT']['data']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataframe with overview of Platforms' attributes\n",
    "An overview dataframe *overview_df* is then generated to show the detailed information about each campaign at sea: *platform code & name*, *data type*, *title*, *instrument*, *longitude* & *latitude*, and *vertical min & max*)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Platform_code</th>\n",
       "      <th>Platform_name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Data_type</th>\n",
       "      <th>Title</th>\n",
       "      <th>Instrument</th>\n",
       "      <th>Vertical_min</th>\n",
       "      <th>Vertical_max</th>\n",
       "      <th>Lon_min</th>\n",
       "      <th>Lon_max</th>\n",
       "      <th>Lat_min</th>\n",
       "      <th>Lat_max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AA</th>\n",
       "      <td>58AA</td>\n",
       "      <td>H\\\\303\\\\245kon Mosby</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>OceanSITES vertical profile</td>\n",
       "      <td>Arctic Ocean - In Situ Observation Copernicus</td>\n",
       "      <td>CTD Seabird</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2811.0</td>\n",
       "      <td>-11.884</td>\n",
       "      <td>17.833</td>\n",
       "      <td>53.996</td>\n",
       "      <td>69.999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GS</th>\n",
       "      <td>58GS</td>\n",
       "      <td>G.O. Sars</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>OceanSITES vertical profile</td>\n",
       "      <td>Arctic Ocean - In Situ Observation Copernicus</td>\n",
       "      <td>CTD Seabird</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3688.0</td>\n",
       "      <td>-17.979</td>\n",
       "      <td>22.532</td>\n",
       "      <td>59.280</td>\n",
       "      <td>78.336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GT</th>\n",
       "      <td>58GT</td>\n",
       "      <td>Sarsen</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>OceanSITES vertical profile</td>\n",
       "      <td>Arctic Ocean - In Situ Observation Copernicus</td>\n",
       "      <td>CTD Seabird</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2962.0</td>\n",
       "      <td>-9.520</td>\n",
       "      <td>35.016</td>\n",
       "      <td>56.667</td>\n",
       "      <td>78.655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HJ</th>\n",
       "      <td>58HJ</td>\n",
       "      <td>Helmer Hanssen</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>OceanSITES vertical profile</td>\n",
       "      <td>Arctic Ocean - In Situ Observation Copernicus</td>\n",
       "      <td>CTD Seabird</td>\n",
       "      <td>1.0</td>\n",
       "      <td>792.0</td>\n",
       "      <td>6.303</td>\n",
       "      <td>39.826</td>\n",
       "      <td>63.889</td>\n",
       "      <td>81.179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JH</th>\n",
       "      <td>58JH</td>\n",
       "      <td>Johan Hjort</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>OceanSITES vertical profile</td>\n",
       "      <td>Arctic Ocean - In Situ Observation Copernicus</td>\n",
       "      <td>CTD Seabird</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3766.0</td>\n",
       "      <td>-15.502</td>\n",
       "      <td>47.017</td>\n",
       "      <td>50.998</td>\n",
       "      <td>77.829</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Platform_code         Platform_name    Year                    Data_type  \\\n",
       "AA          58AA  H\\\\303\\\\245kon Mosby  2003.0  OceanSITES vertical profile   \n",
       "GS          58GS             G.O. Sars  2003.0  OceanSITES vertical profile   \n",
       "GT          58GT                Sarsen  2003.0  OceanSITES vertical profile   \n",
       "HJ          58HJ        Helmer Hanssen  2003.0  OceanSITES vertical profile   \n",
       "JH          58JH           Johan Hjort  2003.0  OceanSITES vertical profile   \n",
       "\n",
       "                                            Title   Instrument  Vertical_min  \\\n",
       "AA  Arctic Ocean - In Situ Observation Copernicus  CTD Seabird           0.0   \n",
       "GS  Arctic Ocean - In Situ Observation Copernicus  CTD Seabird           1.0   \n",
       "GT  Arctic Ocean - In Situ Observation Copernicus  CTD Seabird           1.0   \n",
       "HJ  Arctic Ocean - In Situ Observation Copernicus  CTD Seabird           1.0   \n",
       "JH  Arctic Ocean - In Situ Observation Copernicus  CTD Seabird           1.0   \n",
       "\n",
       "    Vertical_max  Lon_min  Lon_max  Lat_min  Lat_max  \n",
       "AA        2811.0  -11.884   17.833   53.996   69.999  \n",
       "GS        3688.0  -17.979   22.532   59.280   78.336  \n",
       "GT        2962.0   -9.520   35.016   56.667   78.655  \n",
       "HJ         792.0    6.303   39.826   63.889   81.179  \n",
       "JH        3766.0  -15.502   47.017   50.998   77.829  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create database with the selected campaigns & years\n",
    "overview_df = pd.DataFrame()\n",
    "overview_df = getAttributes(overview_df, data_dict)\n",
    "overview_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define Variables\n",
    "The four variables that are available in this dataset are: \n",
    "* **PRES**: Sea Water Pressure\n",
    "* **TEMP**: Sea Water Temperature\n",
    "* **PSAL**: Sea Water Practical Salinity\n",
    "* **CNDC**: Sea Water Electrical Conductivity "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ensure the main variables exist in our dataset \n",
    "vars_main = ['PRES', 'TEMP', 'PSAL', 'CNDC']\n",
    "assert all(elem in data_dict[key]['data'].variables for elem in vars_main for key in data_dict.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filtered dataframe (*df_toPlot*)\n",
    "Define the filtered dataframe to use for the analysis (eg *position_df_bbox*, *position_df_bbox_mm*, *position_df_bbox_hh*) to the **df_toPlot** variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BBOX: BBOX=North Sea\n",
      "Selected positions (out of available positions): 564 out of 3209.\n"
     ]
    },
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Time</th>\n",
       "      <th>Platform</th>\n",
       "      <th>Index_ABS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Index_Relative</th>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-0.9848</td>\n",
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       "      <td>AA</td>\n",
       "      <td>25</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
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       "      <td>AA</td>\n",
       "      <td>27</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.0065</td>\n",
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       "      <td>AA</td>\n",
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       "      <td>0.3293</td>\n",
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       "      <td>AA</td>\n",
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       "      <th>986</th>\n",
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       "      <td>GT</td>\n",
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       "      <th>987</th>\n",
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       "      <td>GT</td>\n",
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       "      <th>988</th>\n",
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       "      <td>GT</td>\n",
       "      <td>2077</td>\n",
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       "      <th>989</th>\n",
       "      <td>-0.5513</td>\n",
       "      <td>57.132500</td>\n",
       "      <td>2003-10-04 20:41:28</td>\n",
       "      <td>GT</td>\n",
       "      <td>2078</td>\n",
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       "      <th>990</th>\n",
       "      <td>-0.5153</td>\n",
       "      <td>57.690300</td>\n",
       "      <td>2003-10-05 02:31:28</td>\n",
       "      <td>GT</td>\n",
       "      <td>2079</td>\n",
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       "<p>564 rows × 5 columns</p>\n",
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      ],
      "text/plain": [
       "                Longitude   Latitude                Time Platform  Index_ABS\n",
       "Index_Relative                                                              \n",
       "25                -0.9848  59.284801 2003-01-12 19:55:32       AA         25\n",
       "26                -0.6563  59.284801 2003-01-12 21:10:15       AA         26\n",
       "27                -0.3618  59.284801 2003-01-12 22:13:23       AA         27\n",
       "28                 0.0065  59.281700 2003-01-12 23:30:54       AA         28\n",
       "29                 0.3293  59.284698 2003-01-13 00:56:57       AA         29\n",
       "...                   ...        ...                 ...      ...        ...\n",
       "986                0.5003  57.240501 2003-10-03 22:24:21       GT       2075\n",
       "987                0.4992  56.780300 2003-10-04 02:38:20       GT       2076\n",
       "988               -0.3618  56.744801 2003-10-04 10:01:20       GT       2077\n",
       "989               -0.5513  57.132500 2003-10-04 20:41:28       GT       2078\n",
       "990               -0.5153  57.690300 2003-10-05 02:31:28       GT       2079\n",
       "\n",
       "[564 rows x 5 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define dataframe to plot based on one of the previously defined filters\n",
    "df_toPlot = position_df_bbox\n",
    "print('BBOX:', bbox_key)\n",
    "\n",
    "sel_outof_all = f'{len(df_toPlot)} out of {len(position_df)}.'\n",
    "print(f'Selected positions (out of available positions): {sel_outof_all}')\n",
    "\n",
    "display(df_toPlot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Range 0-20 Test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define Platform Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "pc = 'GT'; assert pc in df_toPlot.Platform.unique() # platform code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define Selected Variables (*vars_sel*)\n",
    "The list of variables need to be specified in the *vars_sel* variable. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define variables to include in the analysis\n",
    "vars_sel = ['TEMP', 'PRES']; assert all([elem in vars_main for elem in vars_sel])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'GS': {'TEMP': False, 'PRES': False},\n",
       " 'GT': {'TEMP': False, 'PRES': False},\n",
       " 'HJ': {'TEMP': False, 'PRES': False},\n",
       " 'JH': {'TEMP': False, 'PRES': False}}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate vmin dictionary (needed to avoid doing the vmin adjustment more than once)\n",
    "vmin_dict = {}\n",
    "\n",
    "# select only those platforms where vmin == 1\n",
    "vmin_pc = overview_df[overview_df['Vertical_min'] == 1.0].index\n",
    "\n",
    "for i in vmin_pc:\n",
    "    vmin_dict[i] = {}\n",
    "    \n",
    "    for v in vars_sel:\n",
    "        vmin_dict[i][v] = False\n",
    "\n",
    "vmin_dict   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define DEPTH range\n",
    "**INSERT BELOW** the desired DEPTH range (in meters) in the two fields below, noting that:\n",
    "\n",
    "* **depth_start**: either this is equal to the lower bound (ie 0)\n",
    "* **depth_stop**: or is equal to the upper bound (ie depth_stop_dict[pc])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing DEPTH range for analyis:\n",
      " - Pl. AA; DEPTH range of interest: 0 - 2809\n",
      " - Pl. GS; DEPTH range of interest: 0 - 3683\n",
      " - Pl. GT; DEPTH range of interest: 0 - 2956\n",
      " - Pl. HJ; DEPTH range of interest: 0 - 789\n",
      " - Pl. JH; DEPTH range of interest: 0 - 3763\n"
     ]
    }
   ],
   "source": [
    "depth_start = 0\n",
    "depth_stop = 20 #depth_stop_dict[pc]\n",
    "\n",
    "print('Printing DEPTH range for analyis:')\n",
    "for k in depth_stop_dict.keys(): # make sure NOT to use pc (but eg k) here because pc has been already defined\n",
    "    print(f' - Pl. {k}; DEPTH range of interest: {depth_start} - {depth_stop_dict[k]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": "true"
   },
   "source": [
    "### Data Filtering\n",
    "This section allows filtering one or more variables for each platform within a given DEPTH range, to the data that was previously filtered in the Filtered Position section (eg by \"BBOX\", by \"BBOX and Month\", or by \"BBOX and Hour\"). \n",
    "\n",
    "The following are the two types of filters that are given as examples: \n",
    "* Filtered data by BBOX and One Variable\n",
    "* Filtered data by BBOX (All Variables)\n",
    "\n",
    "The output of both filters is a *filtered_xarr* xarray dataset, containing one or all the variables within the specified DEPTH range. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data filtered by BBOX and One Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define var to plot\n",
    "var = 'TEMP'; assert var in vars_sel # variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Platform: GT - Vertical min = 1.0 --> aligning and add nan\n",
      "this is a test\n"
     ]
    },
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;TEMP&#x27; (TIME: 158, DEPTH: 21)&gt;\n",
       "array([[   nan, 17.678, 17.586, ..., 15.494, 15.467, 15.423],\n",
       "       [   nan, 16.354, 16.338, ..., 15.974, 15.583, 15.207],\n",
       "       [   nan, 16.146, 16.094, ..., 15.258, 15.067, 14.912],\n",
       "       ...,\n",
       "       [   nan, 12.951, 12.947, ..., 12.967, 12.965, 12.963],\n",
       "       [   nan, 12.985, 12.983, ..., 12.987, 12.988, 12.988],\n",
       "       [   nan, 12.891, 12.882, ..., 12.902, 12.902, 12.902]])\n",
       "Coordinates:\n",
       "  * TIME     (TIME) float64 1.954e+04 1.954e+04 ... 1.963e+04 1.964e+04\n",
       "Dimensions without coordinates: DEPTH\n",
       "Attributes:\n",
       "    standard_name:        sea_water_temperature\n",
       "    long_name:            Sea temperature\n",
       "    units:                degrees_C\n",
       "    valid_min:            -2500\n",
       "    valid_max:            40000\n",
       "    ancillary_variables:  TEMP_QC TEMP_DM\n",
       "    data_mode:            D</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'TEMP'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>TIME</span>: 158</li><li><span>DEPTH</span>: 21</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-941d7b34-73b6-4538-b8bc-8b6b4226508a' class='xr-array-in' type='checkbox' checked><label for='section-941d7b34-73b6-4538-b8bc-8b6b4226508a' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan 17.68 17.59 17.54 17.52 17.48 ... 12.9 12.9 12.9 12.9 12.9 12.9</span></div><div class='xr-array-data'><pre>array([[   nan, 17.678, 17.586, ..., 15.494, 15.467, 15.423],\n",
       "       [   nan, 16.354, 16.338, ..., 15.974, 15.583, 15.207],\n",
       "       [   nan, 16.146, 16.094, ..., 15.258, 15.067, 14.912],\n",
       "       ...,\n",
       "       [   nan, 12.951, 12.947, ..., 12.967, 12.965, 12.963],\n",
       "       [   nan, 12.985, 12.983, ..., 12.987, 12.988, 12.988],\n",
       "       [   nan, 12.891, 12.882, ..., 12.902, 12.902, 12.902]])</pre></div></div></li><li class='xr-section-item'><input id='section-6fbdd6d5-8668-470b-914c-a34dfb76cf0d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6fbdd6d5-8668-470b-914c-a34dfb76cf0d' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>TIME</span></div><div class='xr-var-dims'>(TIME)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.954e+04 1.954e+04 ... 1.964e+04</div><input id='attrs-c73c9089-738e-4f6c-b21d-0d3ba1b84a41' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c73c9089-738e-4f6c-b21d-0d3ba1b84a41' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97cf9148-79be-46b2-bb03-6ad1b6836065' class='xr-var-data-in' type='checkbox'><label for='data-97cf9148-79be-46b2-bb03-6ad1b6836065' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>units :</span></dt><dd>days since 1950-01-01T00:00:00Z</dd><dt><span>valid_min :</span></dt><dd>-90000.0</dd><dt><span>valid_max :</span></dt><dd>90000.0</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>QC_indicator :</span></dt><dd>1</dd><dt><span>uncertainty :</span></dt><dd></dd><dt><span>comment :</span></dt><dd></dd><dt><span>ancillary_variables :</span></dt><dd>TIME_QC</dd><dt><span>calendar :</span></dt><dd>standard</dd></dl></div><div class='xr-var-data'><pre>array([19540.623079, 19541.126748, 19541.140532, 19541.160231, 19541.193125,\n",
       "       19541.22309 , 19541.267199, 19541.297604, 19541.329109, 19541.423391,\n",
       "       19541.447384, 19541.471505, 19541.484294, 19541.590764, 19541.690312,\n",
       "       19541.736169, 19541.787662, 19541.840868, 19541.928947, 19541.986806,\n",
       "       19542.082095, 19542.197083, 19542.269178, 19542.327222, 19542.437859,\n",
       "       19542.473819, 19542.51412 , 19542.577558, 19542.710567, 19542.774028,\n",
       "       19542.938229, 19543.07853 , 19543.317581, 19543.677072, 19543.796586,\n",
       "       19543.979329, 19544.197894, 19544.332951, 19544.520625, 19544.59015 ,\n",
       "       19544.723194, 19544.790451, 19544.859387, 19544.930093, 19544.994329,\n",
       "       19545.050799, 19546.403819, 19546.567083, 19546.748472, 19546.874803,\n",
       "       19547.022535, 19547.277141, 19547.399352, 19547.555579, 19547.607836,\n",
       "       19547.774896, 19548.04515 , 19548.220081, 19548.414502, 19548.610602,\n",
       "       19548.814884, 19548.957963, 19549.110752, 19550.657049, 19550.847269,\n",
       "       19551.140046, 19551.293935, 19551.49713 , 19551.75515 , 19551.926505,\n",
       "       19552.1639  , 19552.304201, 19552.599931, 19552.722535, 19552.892812,\n",
       "       19553.105741, 19553.131713, 19553.160104, 19553.188113, 19553.254549,\n",
       "       19553.287928, 19553.318866, 19553.347245, 19553.372951, 19553.399711,\n",
       "       19553.423588, 19553.445903, 19553.470787, 19553.491713, 19553.522326,\n",
       "       19553.557488, 19553.627211, 19553.674236, 19553.775625, 19553.81853 ,\n",
       "       19553.862975, 19553.907928, 19553.953171, 19553.997431, 19554.076227,\n",
       "       19554.120301, 19554.165544, 19554.206042, 19554.242813, 19554.282801,\n",
       "       19554.80978 , 19554.966898, 19555.193183, 19555.378507, 19555.460081,\n",
       "       19555.716516, 19555.898403, 19556.084456, 19556.221991, 19622.241875,\n",
       "       19622.385312, 19622.557604, 19622.721644, 19622.900382, 19623.068241,\n",
       "       19623.627963, 19623.788542, 19623.976667, 19624.169074, 19624.346782,\n",
       "       19624.572222, 19624.738333, 19624.962894, 19625.203206, 19625.368252,\n",
       "       19625.594826, 19625.764468, 19626.034213, 19626.241574, 19626.418796,\n",
       "       19626.599097, 19626.724132, 19626.909977, 19627.127951, 19630.760093,\n",
       "       19630.983299, 19631.246273, 19631.441713, 19631.660127, 19631.848692,\n",
       "       19632.011898, 19632.297894, 19632.499282, 19632.700764, 19632.935243,\n",
       "       19633.131875, 19633.395799, 19633.728692, 19633.933576, 19634.109954,\n",
       "       19634.417593, 19634.86213 , 19635.105185])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fc38943d-fbbc-4eb3-9e40-635f7d97612e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fc38943d-fbbc-4eb3-9e40-635f7d97612e' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>sea_water_temperature</dd><dt><span>long_name :</span></dt><dd>Sea temperature</dd><dt><span>units :</span></dt><dd>degrees_C</dd><dt><span>valid_min :</span></dt><dd>-2500</dd><dt><span>valid_max :</span></dt><dd>40000</dd><dt><span>ancillary_variables :</span></dt><dd>TEMP_QC TEMP_DM</dd><dt><span>data_mode :</span></dt><dd>D</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'TEMP' (TIME: 158, DEPTH: 21)>\n",
       "array([[   nan, 17.678, 17.586, ..., 15.494, 15.467, 15.423],\n",
       "       [   nan, 16.354, 16.338, ..., 15.974, 15.583, 15.207],\n",
       "       [   nan, 16.146, 16.094, ..., 15.258, 15.067, 14.912],\n",
       "       ...,\n",
       "       [   nan, 12.951, 12.947, ..., 12.967, 12.965, 12.963],\n",
       "       [   nan, 12.985, 12.983, ..., 12.987, 12.988, 12.988],\n",
       "       [   nan, 12.891, 12.882, ..., 12.902, 12.902, 12.902]])\n",
       "Coordinates:\n",
       "  * TIME     (TIME) float64 1.954e+04 1.954e+04 ... 1.963e+04 1.964e+04\n",
       "Dimensions without coordinates: DEPTH\n",
       "Attributes:\n",
       "    standard_name:        sea_water_temperature\n",
       "    long_name:            Sea temperature\n",
       "    units:                degrees_C\n",
       "    valid_min:            -2500\n",
       "    valid_max:            40000\n",
       "    ancillary_variables:  TEMP_QC TEMP_DM\n",
       "    data_mode:            D"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate a filtered xarray with a single variable for selected Platform\n",
    "align_and_nan=True\n",
    "check_alignment(data_dict, pc, var, align_and_nan, vmin_dict)\n",
    "\n",
    "filtered_xarr = filter_xarr(df_toPlot, \n",
    "                            data_dict, \n",
    "                            platform=pc, \n",
    "                            var=var, \n",
    "                            depth_range=[depth_start,\n",
    "                                         depth_stop])\n",
    "filtered_xarr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data filtered by BBOX and All Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Platform: GT - Vertical min = 1.0 --> aligning and add nan\n",
      "this is a test\n"
     ]
    },
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       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block;\n",
       "  min-width: 300px;\n",
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       ".xr-text-repr-fallback {\n",
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       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
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       "\n",
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       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
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       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
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       "\n",
       ".xr-section-item input:enabled + label {\n",
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       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
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       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
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       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
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       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
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       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
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       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
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       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
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       "\n",
       ".xr-array-wrap {\n",
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       "  display: grid;\n",
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       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
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       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
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       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (DEPTH: 21, TIME: 158)\n",
       "Coordinates:\n",
       "  * TIME     (TIME) float64 1.954e+04 1.954e+04 ... 1.963e+04 1.964e+04\n",
       "Dimensions without coordinates: DEPTH\n",
       "Data variables:\n",
       "    TEMP     (TIME, DEPTH) float64 nan 17.68 17.59 17.54 ... 12.9 12.9 12.9 12.9\n",
       "    PRES     (TIME, DEPTH) float32 nan 2.0 3.0 4.0 5.0 ... 19.0 20.0 21.0 22.0\n",
       "Attributes: (12/51)\n",
       "    title:                          Arctic Ocean - In Situ Observation Copern...\n",
       "    qc_manual:                      Recommendations for in-situ data Near Rea...\n",
       "    contact:                        cmems-service@imr.no\n",
       "    format_version:                 1.4\n",
       "    distribution_statement:         These data follow Copernicus standards; t...\n",
       "    citation:                       These data were collected and made freely...\n",
       "    ...                             ...\n",
       "    last_latitude_observation:      57.690\n",
       "    geospatial_lon_min:             -9.520\n",
       "    geospatial_lon_max:             35.016\n",
       "    last_longitude_observation:     -0.515\n",
       "    geospatial_vertical_min:        1.000\n",
       "    geospatial_vertical_max:        2962.000</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-929c0896-3b98-4a6f-999b-bcb65bb89204' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-929c0896-3b98-4a6f-999b-bcb65bb89204' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>DEPTH</span>: 21</li><li><span class='xr-has-index'>TIME</span>: 158</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-0b9a81b4-3d12-4bc0-8ca9-77f2a9e037d5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0b9a81b4-3d12-4bc0-8ca9-77f2a9e037d5' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>TIME</span></div><div class='xr-var-dims'>(TIME)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.954e+04 1.954e+04 ... 1.964e+04</div><input id='attrs-bf70d0de-5c66-420f-aa6a-107ff2360ce8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bf70d0de-5c66-420f-aa6a-107ff2360ce8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e681181-fef8-48bf-9fe3-7c0aca9c89c0' class='xr-var-data-in' type='checkbox'><label for='data-3e681181-fef8-48bf-9fe3-7c0aca9c89c0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>units :</span></dt><dd>days since 1950-01-01T00:00:00Z</dd><dt><span>valid_min :</span></dt><dd>-90000.0</dd><dt><span>valid_max :</span></dt><dd>90000.0</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>QC_indicator :</span></dt><dd>1</dd><dt><span>uncertainty :</span></dt><dd></dd><dt><span>comment :</span></dt><dd></dd><dt><span>ancillary_variables :</span></dt><dd>TIME_QC</dd><dt><span>calendar :</span></dt><dd>standard</dd></dl></div><div class='xr-var-data'><pre>array([19540.623079, 19541.126748, 19541.140532, 19541.160231, 19541.193125,\n",
       "       19541.22309 , 19541.267199, 19541.297604, 19541.329109, 19541.423391,\n",
       "       19541.447384, 19541.471505, 19541.484294, 19541.590764, 19541.690312,\n",
       "       19541.736169, 19541.787662, 19541.840868, 19541.928947, 19541.986806,\n",
       "       19542.082095, 19542.197083, 19542.269178, 19542.327222, 19542.437859,\n",
       "       19542.473819, 19542.51412 , 19542.577558, 19542.710567, 19542.774028,\n",
       "       19542.938229, 19543.07853 , 19543.317581, 19543.677072, 19543.796586,\n",
       "       19543.979329, 19544.197894, 19544.332951, 19544.520625, 19544.59015 ,\n",
       "       19544.723194, 19544.790451, 19544.859387, 19544.930093, 19544.994329,\n",
       "       19545.050799, 19546.403819, 19546.567083, 19546.748472, 19546.874803,\n",
       "       19547.022535, 19547.277141, 19547.399352, 19547.555579, 19547.607836,\n",
       "       19547.774896, 19548.04515 , 19548.220081, 19548.414502, 19548.610602,\n",
       "       19548.814884, 19548.957963, 19549.110752, 19550.657049, 19550.847269,\n",
       "       19551.140046, 19551.293935, 19551.49713 , 19551.75515 , 19551.926505,\n",
       "       19552.1639  , 19552.304201, 19552.599931, 19552.722535, 19552.892812,\n",
       "       19553.105741, 19553.131713, 19553.160104, 19553.188113, 19553.254549,\n",
       "       19553.287928, 19553.318866, 19553.347245, 19553.372951, 19553.399711,\n",
       "       19553.423588, 19553.445903, 19553.470787, 19553.491713, 19553.522326,\n",
       "       19553.557488, 19553.627211, 19553.674236, 19553.775625, 19553.81853 ,\n",
       "       19553.862975, 19553.907928, 19553.953171, 19553.997431, 19554.076227,\n",
       "       19554.120301, 19554.165544, 19554.206042, 19554.242813, 19554.282801,\n",
       "       19554.80978 , 19554.966898, 19555.193183, 19555.378507, 19555.460081,\n",
       "       19555.716516, 19555.898403, 19556.084456, 19556.221991, 19622.241875,\n",
       "       19622.385312, 19622.557604, 19622.721644, 19622.900382, 19623.068241,\n",
       "       19623.627963, 19623.788542, 19623.976667, 19624.169074, 19624.346782,\n",
       "       19624.572222, 19624.738333, 19624.962894, 19625.203206, 19625.368252,\n",
       "       19625.594826, 19625.764468, 19626.034213, 19626.241574, 19626.418796,\n",
       "       19626.599097, 19626.724132, 19626.909977, 19627.127951, 19630.760093,\n",
       "       19630.983299, 19631.246273, 19631.441713, 19631.660127, 19631.848692,\n",
       "       19632.011898, 19632.297894, 19632.499282, 19632.700764, 19632.935243,\n",
       "       19633.131875, 19633.395799, 19633.728692, 19633.933576, 19634.109954,\n",
       "       19634.417593, 19634.86213 , 19635.105185])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5648f778-f024-4089-9077-74aac014a8c9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5648f778-f024-4089-9077-74aac014a8c9' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>TEMP</span></div><div class='xr-var-dims'>(TIME, DEPTH)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan 17.68 17.59 ... 12.9 12.9 12.9</div><input id='attrs-fa282856-f746-4604-bbc8-d57a17f1f503' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fa282856-f746-4604-bbc8-d57a17f1f503' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4300c313-7c8c-44d9-a565-e5692c32a90f' class='xr-var-data-in' type='checkbox'><label for='data-4300c313-7c8c-44d9-a565-e5692c32a90f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>sea_water_temperature</dd><dt><span>long_name :</span></dt><dd>Sea temperature</dd><dt><span>units :</span></dt><dd>degrees_C</dd><dt><span>valid_min :</span></dt><dd>-2500</dd><dt><span>valid_max :</span></dt><dd>40000</dd><dt><span>ancillary_variables :</span></dt><dd>TEMP_QC TEMP_DM</dd><dt><span>data_mode :</span></dt><dd>D</dd></dl></div><div class='xr-var-data'><pre>array([[   nan, 17.678, 17.586, ..., 15.494, 15.467, 15.423],\n",
       "       [   nan, 16.354, 16.338, ..., 15.974, 15.583, 15.207],\n",
       "       [   nan, 16.146, 16.094, ..., 15.258, 15.067, 14.912],\n",
       "       ...,\n",
       "       [   nan, 12.951, 12.947, ..., 12.967, 12.965, 12.963],\n",
       "       [   nan, 12.985, 12.983, ..., 12.987, 12.988, 12.988],\n",
       "       [   nan, 12.891, 12.882, ..., 12.902, 12.902, 12.902]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PRES</span></div><div class='xr-var-dims'>(TIME, DEPTH)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan 2.0 3.0 4.0 ... 20.0 21.0 22.0</div><input id='attrs-7d38b5f3-cef8-4e91-85d4-6d95a34d76a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7d38b5f3-cef8-4e91-85d4-6d95a34d76a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b685b03b-ffbe-4633-8bd3-c990994c30a0' class='xr-var-data-in' type='checkbox'><label for='data-b685b03b-ffbe-4633-8bd3-c990994c30a0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>uncertainty :</span></dt><dd> </dd><dt><span>comment :</span></dt><dd> </dd><dt><span>ancillary_variables :</span></dt><dd>PRES_QC</dd><dt><span>standard_name :</span></dt><dd>sea_water_pressure</dd><dt><span>long_name :</span></dt><dd>Sea pressure</dd><dt><span>units :</span></dt><dd>dbar</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>data_mode :</span></dt><dd>D</dd></dl></div><div class='xr-var-data'><pre>array([[nan,  2.,  3., ..., 19., 20., 21.],\n",
       "       [nan,  1.,  2., ..., 18., 19., 20.],\n",
       "       [nan,  1.,  2., ..., 18., 19., 20.],\n",
       "       ...,\n",
       "       [nan,  2.,  3., ..., 19., 20., 21.],\n",
       "       [nan,  2.,  3., ..., 19., 20., 21.],\n",
       "       [nan,  3.,  4., ..., 20., 21., 22.]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b06156ba-2f81-4288-bbdc-78c16a85ec1e' class='xr-section-summary-in' type='checkbox'  ><label for='section-b06156ba-2f81-4288-bbdc-78c16a85ec1e' class='xr-section-summary' >Attributes: <span>(51)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>Arctic Ocean - In Situ Observation Copernicus</dd><dt><span>qc_manual :</span></dt><dd>Recommendations for in-situ data Near Real Time Quality Control https://doi.org/10.13155/36230</dd><dt><span>contact :</span></dt><dd>cmems-service@imr.no</dd><dt><span>format_version :</span></dt><dd>1.4</dd><dt><span>distribution_statement :</span></dt><dd>These data follow Copernicus standards; they are public and free of charge. User assumes all risk for use of data. User must display citation in any publication or product using data. User must contact PI prior to any commercial use of data.</dd><dt><span>citation :</span></dt><dd>These data were collected and made freely available by the Copernicus project and the programs that contribute to it </dd><dt><span>naming_authority :</span></dt><dd>Copernicus Marine In Situ</dd><dt><span>data_assembly_center :</span></dt><dd>IMR</dd><dt><span>update_interval :</span></dt><dd>void</dd><dt><span>area :</span></dt><dd>Arctic Ocean</dd><dt><span>author :</span></dt><dd></dd><dt><span>Conventions :</span></dt><dd>CF-1.6 Copernicus-InSituTAC-FormatManual-1.41 Copernicus-InSituTAC-SRD-1.5 Copernicus-InSituTAC-ParametersList-3.2.0</dd><dt><span>data_mode :</span></dt><dd>D</dd><dt><span>comment :</span></dt><dd></dd><dt><span>history :</span></dt><dd>\\0122021-04-12 07:22:38 GMT Hyrax-1.14.0 http://opendap1.nodc.no:80/opendap/physics/point/yearly/58GT_CTD_2003.nc.nc4?</dd><dt><span>references :</span></dt><dd>http://marine.copernicus.eu http://www.marineinsitu.eu</dd><dt><span>netcdf_version :</span></dt><dd>netCDF-4 classic model</dd><dt><span>quality_index :</span></dt><dd>A</dd><dt><span>quality_control_indicator :</span></dt><dd>0</dd><dt><span>doi :</span></dt><dd></dd><dt><span>summary :</span></dt><dd></dd><dt><span>wmo_platform_code :</span></dt><dd></dd><dt><span>ices_platform_code :</span></dt><dd></dd><dt><span>site_code :</span></dt><dd></dd><dt><span>bottom_depth :</span></dt><dd></dd><dt><span>wmo_inst_type :</span></dt><dd></dd><dt><span>platform_code :</span></dt><dd>58GT</dd><dt><span>platform_name :</span></dt><dd>Sarsen</dd><dt><span>institution :</span></dt><dd>Institute of Marine Research (Norway)</dd><dt><span>institution_edmo_code :</span></dt><dd>1351</dd><dt><span>source :</span></dt><dd>research vessel</dd><dt><span>institution_references :</span></dt><dd>http://www.imr.no</dd><dt><span>institution_country :</span></dt><dd>Norway</dd><dt><span>source_platform_category_code :</span></dt><dd>31</dd><dt><span>pi_name :</span></dt><dd></dd><dt><span>data_type :</span></dt><dd>OceanSITES vertical profile</dd><dt><span>cdm_data_type :</span></dt><dd>profile</dd><dt><span>instrument :</span></dt><dd>CTD Seabird</dd><dt><span>id :</span></dt><dd>58GT_CTD_2003</dd><dt><span>time_coverage_start :</span></dt><dd>2003-01-19T09:59:03Z</dd><dt><span>time_coverage_end :</span></dt><dd>2003-10-05T02:31:28Z</dd><dt><span>last_date_observation :</span></dt><dd>2003-10-05T02:31:28Z</dd><dt><span>date_update :</span></dt><dd>2021-03-23T08:36:42Z</dd><dt><span>geospatial_lat_min :</span></dt><dd>56.667</dd><dt><span>geospatial_lat_max :</span></dt><dd>78.655</dd><dt><span>last_latitude_observation :</span></dt><dd>57.690</dd><dt><span>geospatial_lon_min :</span></dt><dd>-9.520</dd><dt><span>geospatial_lon_max :</span></dt><dd>35.016</dd><dt><span>last_longitude_observation :</span></dt><dd>-0.515</dd><dt><span>geospatial_vertical_min :</span></dt><dd>1.000</dd><dt><span>geospatial_vertical_max :</span></dt><dd>2962.000</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (DEPTH: 21, TIME: 158)\n",
       "Coordinates:\n",
       "  * TIME     (TIME) float64 1.954e+04 1.954e+04 ... 1.963e+04 1.964e+04\n",
       "Dimensions without coordinates: DEPTH\n",
       "Data variables:\n",
       "    TEMP     (TIME, DEPTH) float64 nan 17.68 17.59 17.54 ... 12.9 12.9 12.9 12.9\n",
       "    PRES     (TIME, DEPTH) float32 nan 2.0 3.0 4.0 5.0 ... 19.0 20.0 21.0 22.0\n",
       "Attributes: (12/51)\n",
       "    title:                          Arctic Ocean - In Situ Observation Copern...\n",
       "    qc_manual:                      Recommendations for in-situ data Near Rea...\n",
       "    contact:                        cmems-service@imr.no\n",
       "    format_version:                 1.4\n",
       "    distribution_statement:         These data follow Copernicus standards; t...\n",
       "    citation:                       These data were collected and made freely...\n",
       "    ...                             ...\n",
       "    last_latitude_observation:      57.690\n",
       "    geospatial_lon_min:             -9.520\n",
       "    geospatial_lon_max:             35.016\n",
       "    last_longitude_observation:     -0.515\n",
       "    geospatial_vertical_min:        1.000\n",
       "    geospatial_vertical_max:        2962.000"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate a filtered xarray with all variables for selected Platform\n",
    "for var in vars_sel: \n",
    "    \n",
    "    align_and_nan=True\n",
    "    check_alignment(data_dict, pc, var, align_and_nan, vmin_dict)\n",
    "\n",
    "filtered_xarr = filter_xarr(df_toPlot, \n",
    "                            data_dict, \n",
    "                            platform=pc, \n",
    "                            var=vars_sel, \n",
    "                            depth_range=[depth_start,\n",
    "                                         depth_stop])\n",
    "filtered_xarr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reference Plots\n",
    "The reference plots are generated for the available **variables** of the **filtered** xarrays. On the y-axis is shown the TIME of the measurement (in *float* format, which needs to be converted to *datetime* format), and on the x-axis is the DEPTH of the measurement."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting individual Variables per individual Platform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are multiple VARs available from the applied filtered: ['TEMP', 'PRES']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "try:\n",
    "    var = filtered_xarr.name\n",
    "    print(f\"There's only one VAR available from the applied filtered: {var}\")\n",
    "    \n",
    "    title = f'Platform={pc}, Var={var} (Individual Platform)\\nFilter: Year={year}, {bbox_key}, Depth Range {depth_start}-{depth_stop}\\nSel/All={sel_outof_all}'\n",
    "    \n",
    "    plotFilteredVar(filtered_xarr, title=title)\n",
    "    \n",
    "except:\n",
    "    myvars = list(filtered_xarr.keys())\n",
    "    print(f\"There are multiple VARs available from the applied filtered: {myvars}\")\n",
    "    \n",
    "    for var in vars_sel:\n",
    "        \n",
    "        title = f'Platform={pc}, Var={var} (Individual Platform)\\nFilter: Year={year}, {bbox_key}, Depth Range {depth_start}-{depth_stop}\\nSel/All={sel_outof_all}'\n",
    "\n",
    "        plotFilteredVar(filtered_xarr[var], title=title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting individual Variables across aggregated Platforms\n",
    "Build a dictionary that stores, for each platform and variable combination, the data sliced using the same DEPTH dimension in such a way to generate datasets that can be combined / merged."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Generate **filtered_xarr** for all Platforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Platform: AA - Vertical min = 0.0\n",
      "Platform: AA - Vertical min = 0.0\n",
      "Platform: GS - Vertical min = 1.0 --> aligning and add nan\n",
      "this is a test\n",
      "Platform: GS - Vertical min = 1.0 --> aligning and add nan\n",
      "this is a test\n"
     ]
    }
   ],
   "source": [
    "# Generate a filtered xarray with all variables for selected Platform, for a certain DEPTH range\n",
    "filtered_xarr_dict = {}\n",
    "\n",
    "for pc in df_toPlot['Platform'].unique():\n",
    "    \n",
    "    for var in vars_sel: \n",
    "\n",
    "        align_and_nan=True\n",
    "        check_alignment(data_dict, pc, var, align_and_nan, vmin_dict)\n",
    "        \n",
    "    filtered_xarr_dict[pc] = filter_xarr(df_toPlot, \n",
    "                                         data_dict, \n",
    "                                         platform=pc, \n",
    "                                         var=vars_sel, \n",
    "                                         depth_range=[depth_start,\n",
    "                                                      depth_stop])\n",
    "    \n",
    "# filtered_xarr_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PC AA\tFiltered Dims: TIME=357, DEPTH=21\n",
      "PC GS\tFiltered Dims: TIME=49, DEPTH=21\n",
      "PC GT\tFiltered Dims: TIME=158, DEPTH=21\n"
     ]
    }
   ],
   "source": [
    "data_var_dict = {}\n",
    "\n",
    "for pc in df_toPlot['Platform'].unique(): \n",
    "    \n",
    "    data_var_dict[pc] = {}\n",
    "    data = filtered_xarr_dict[pc]\n",
    "    \n",
    "    print(f'PC {pc}\\tFiltered Dims: TIME={data.dims[\"TIME\"]}, DEPTH={data.dims[\"DEPTH\"]}')\n",
    "\n",
    "    for var in vars_sel:\n",
    "        data_var_dict[pc][var] = filtered_xarr_dict[pc][var]\n",
    "\n",
    "# display(data_var_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Aggregation\n",
    "Two xarray datasets can be merged if they have the same structure, i.e. dimensions. Here we use two aggregation methods that allow doing that, in two different ways: using the **min_depth_elements** and **max_depth_elements**, which are defined as follows: \n",
    "* **min_depth_elements** = least common denominator for the DEPTH dimension found amongst the elements of the selected platforms\n",
    "* **max_depth_elements** = maximum value of the DEPTH dimension found amongst the elements of the selected platforms \n",
    "\n",
    "The two aggregation methods are:\n",
    "* Aggregation with **min_depth_elements**: data are aggregated using the minimum dimension of depth. The xarrays of those platforms that have a larger depth dimension will be truncated to the common minimum dimension.       \n",
    "* Aggregation with **max_depth_elements**: data are aggregated using the maximum dimension of depth. The xarrays of those platforms that have a lower depth dimension will have their dimension increased to the common maximum dimension and filled with nans."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P.Code (TIME, DEPTH)\n",
      "AA (357, 21)\n",
      "GS (49, 21)\n",
      "GT (158, 21)\n",
      "max_depth_elements: 21\n",
      "min_depth_elements: 21\n"
     ]
    }
   ],
   "source": [
    "print(f'P.Code (TIME, DEPTH)')\n",
    "[print(k, data_var_dict[k][\"TEMP\"].shape) for k in data_var_dict.keys()]\n",
    "\n",
    "# Find maximum and minimum DEPTH dimension among the platforms\n",
    "max_depth_elements = max([data_var_dict[k]['TEMP'].shape[1] for k in data_var_dict.keys()]); print('max_depth_elements:', max_depth_elements)\n",
    "min_depth_elements = min([data_var_dict[k]['TEMP'].shape[1] for k in data_var_dict.keys()]); print('min_depth_elements:', min_depth_elements)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NB**: if *min_depth_elements* and *max_depth_elements* are equal to each other, either aggregation methods (with min_depth_elements or with max_depht_elements) will lead to the same result. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Aggregate with **min_depth_elements**\n",
    "Use minimum DEPTH across the platforms' DEPTHs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reshaping using minimum DAPTH dimension (21):\n",
      "(357, 21) --> (357, 21)\n",
      "(357, 21) --> (357, 21)\n",
      "(49, 21) --> (49, 21)\n",
      "(49, 21) --> (49, 21)\n",
      "(158, 21) --> (158, 21)\n",
      "(158, 21) --> (158, 21)\n"
     ]
    }
   ],
   "source": [
    "# Use min_depth_elements across available platforms: Reduce size to the minimum dimension \n",
    "print(f'Reshaping using minimum DAPTH dimension ({min_depth_elements}):')\n",
    "\n",
    "reshaped_dict = {}\n",
    "\n",
    "for pc in data_var_dict.keys():\n",
    "    reshaped_dict[pc] = {}\n",
    "    \n",
    "    for var in vars_sel:\n",
    "        \n",
    "        data = data_var_dict[pc][var].isel(DEPTH=slice(0,min_depth_elements)); #display(data_sel)\n",
    "        \n",
    "        print(f'{data_var_dict[pc][var].shape} --> {data.shape}')\n",
    "        \n",
    "        reshaped_dict[pc][var] = data\n",
    "        \n",
    "# reshaped_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Aggregate with **max_depth_elements**\n",
    "use maximum DEPTH across all platforms' DEPTHs, and fill empty values with nans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reshaping using maximum DAPTH dimension (21):\n",
      "(357, 21) --> (357, 21)\n",
      "(357, 21) --> (357, 21)\n",
      "(49, 21) --> (49, 21)\n",
      "(49, 21) --> (49, 21)\n",
      "(158, 21) --> (158, 21)\n",
      "(158, 21) --> (158, 21)\n"
     ]
    }
   ],
   "source": [
    "print(f'Reshaping using maximum DAPTH dimension ({max_depth_elements}):')\n",
    "\n",
    "reshaped_dict = {}\n",
    "\n",
    "for pc in data_var_dict.keys():\n",
    "    reshaped_dict[pc] = {}\n",
    "    \n",
    "    for var in vars_sel:\n",
    "        \n",
    "        arr = data_var_dict[pc][var]\n",
    "        \n",
    "        # generate an empty array of nans, to then concatenate to the existing data array, to ensure it matches the desired new dimension DEPTH \n",
    "        fill = np.empty((arr.shape[0], max_depth_elements-arr.shape[1]), dtype=arr.dtype); fill[:] = np.nan\n",
    "        newarr_data = np.concatenate((arr.data,fill), axis=1)\n",
    "        print(f'{arr.shape} --> {newarr_data.shape}')\n",
    "        \n",
    "        # Now create the xarray.DataArray\n",
    "        newarr = newXDA(arr, newarr_data, var)\n",
    "        \n",
    "        reshaped_dict[pc][var] = newarr\n",
    "\n",
    "# reshaped_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Plotting Aggregate Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Combine arrays across platforms, for each variable\n",
    "merged_arr = {}\n",
    "\n",
    "for var in vars_sel:\n",
    "    \n",
    "    merged_arr[var] = xr.merge([reshaped_dict[pc][var] for pc in reshaped_dict.keys()])  \n",
    "    \n",
    "#     print(var, type(merged_arr[var]), merged_arr[var][var].shape) # Note you need double 'var' because the first is the key of teh dictionary, the second is the name of the var to plot\n",
    "#     display(merged_arr[var])\n",
    "    \n",
    "    title = f'Var={var} (Merged Platforms)\\nFilter: Year={year}, {bbox_key}, Depth Range {depth_start}-{depth_stop}\\nSel/All={sel_outof_all}'\n",
    "\n",
    "    plotVar_MergedPlatforms(merged_arr[var], var, title=title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Range 50-last Test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define Platform Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "pc = 'GT'; assert pc in df_toPlot.Platform.unique() # platform code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define Selected Variables (*vars_sel*)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define variables to include in the analysis\n",
    "vars_sel = ['TEMP', 'PRES'] #['PSAL', 'CNDC'] # \n",
    "assert all([elem in vars_main for elem in vars_sel])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'GS': {'TEMP': False, 'PRES': False},\n",
       " 'GT': {'TEMP': False, 'PRES': False},\n",
       " 'HJ': {'TEMP': False, 'PRES': False},\n",
       " 'JH': {'TEMP': False, 'PRES': False}}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate vmin dictionary (needed to avoid doing the vmin adjustment more than once)\n",
    "vmin_dict = {}\n",
    "\n",
    "# select only those platforms where vmin == 1\n",
    "vmin_pc = overview_df[overview_df['Vertical_min'] == 1.0].index\n",
    "\n",
    "for i in vmin_pc:\n",
    "    vmin_dict[i] = {}\n",
    "    \n",
    "    for v in vars_sel:\n",
    "        vmin_dict[i][v] = False\n",
    "\n",
    "vmin_dict   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define DEPTH range\n",
    "**INSERT BELOW** the desired DEPTH range (in meters) in the two fields below, noting that:\n",
    "\n",
    "* **depth_start**: either this is equal to the lower bound (ie 0)\n",
    "* **depth_stop**: or is equal to the upper bound (ie depth_stop_dict[pc])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing DEPTH range for analyis:\n",
      " - Pl. AA; DEPTH range of interest: 50 - 2809\n",
      " - Pl. GS; DEPTH range of interest: 50 - 3683\n",
      " - Pl. GT; DEPTH range of interest: 50 - 2956\n",
      " - Pl. HJ; DEPTH range of interest: 50 - 789\n",
      " - Pl. JH; DEPTH range of interest: 50 - 3763\n"
     ]
    }
   ],
   "source": [
    "depth_start = 50\n",
    "depth_stop = depth_stop_dict[pc]\n",
    "\n",
    "print('Printing DEPTH range for analyis:')\n",
    "for k in depth_stop_dict.keys():\n",
    "    print(f' - Pl. {k}; DEPTH range of interest: {depth_start} - {depth_stop_dict[k]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Filtering\n",
    "This section allows filtering one or more variables for each platform within a given DEPTH range, to the data that was previously filtered in the Filtered Position section (eg by \"BBOX\", by \"BBOX and Month\", or by \"BBOX and Hour\"). \n",
    "\n",
    "The following are the two types of filters that are given as examples: \n",
    "* Filtered data by BBOX and One Variable\n",
    "* Filtered data by BBOX (All Variables)\n",
    "\n",
    "The output of both filters is a *filtered_xarr* xarray dataset, containing one or all the variables within the specified DEPTH range. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data filtered by BBOX and One Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define var to plot\n",
    "var = 'TEMP'; assert var in vars_sel # variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Platform: GT - Vertical min = 1.0 --> aligning and add nan\n",
      "this is a test\n"
     ]
    },
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       "       [12.903, 12.904, 12.904, ...,    nan,    nan,    nan]])\n",
       "Coordinates:\n",
       "  * TIME     (TIME) float64 1.954e+04 1.954e+04 ... 1.963e+04 1.964e+04\n",
       "Dimensions without coordinates: DEPTH\n",
       "Attributes:\n",
       "    standard_name:        sea_water_temperature\n",
       "    long_name:            Sea temperature\n",
       "    units:                degrees_C\n",
       "    valid_min:            -2500\n",
       "    valid_max:            40000\n",
       "    ancillary_variables:  TEMP_QC TEMP_DM\n",
       "    data_mode:            D</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'TEMP'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>TIME</span>: 158</li><li><span>DEPTH</span>: 2907</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-0177532b-0421-4d6d-92c2-4bbe1608fa9c' class='xr-array-in' type='checkbox' checked><label for='section-0177532b-0421-4d6d-92c2-4bbe1608fa9c' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan nan nan nan nan ... nan nan nan nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
       "       [ 9.189,  9.165,  9.136, ...,    nan,    nan,    nan],\n",
       "       [ 9.132,  9.099,  9.08 , ...,    nan,    nan,    nan],\n",
       "       ...,\n",
       "       [11.98 , 11.981, 11.981, ...,    nan,    nan,    nan],\n",
       "       [12.992, 12.992, 12.992, ...,    nan,    nan,    nan],\n",
       "       [12.903, 12.904, 12.904, ...,    nan,    nan,    nan]])</pre></div></div></li><li class='xr-section-item'><input id='section-2b1c1be5-a12f-4df8-841a-a32bdbb24e8b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2b1c1be5-a12f-4df8-841a-a32bdbb24e8b' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>TIME</span></div><div class='xr-var-dims'>(TIME)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.954e+04 1.954e+04 ... 1.964e+04</div><input id='attrs-fa46855d-4416-4176-9696-a19135b0f50c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fa46855d-4416-4176-9696-a19135b0f50c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc96bb7e-0685-4194-86e7-12f92e9bd4af' class='xr-var-data-in' type='checkbox'><label for='data-cc96bb7e-0685-4194-86e7-12f92e9bd4af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>units :</span></dt><dd>days since 1950-01-01T00:00:00Z</dd><dt><span>valid_min :</span></dt><dd>-90000.0</dd><dt><span>valid_max :</span></dt><dd>90000.0</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>QC_indicator :</span></dt><dd>1</dd><dt><span>uncertainty :</span></dt><dd></dd><dt><span>comment :</span></dt><dd></dd><dt><span>ancillary_variables :</span></dt><dd>TIME_QC</dd><dt><span>calendar :</span></dt><dd>standard</dd></dl></div><div class='xr-var-data'><pre>array([19540.623079, 19541.126748, 19541.140532, 19541.160231, 19541.193125,\n",
       "       19541.22309 , 19541.267199, 19541.297604, 19541.329109, 19541.423391,\n",
       "       19541.447384, 19541.471505, 19541.484294, 19541.590764, 19541.690312,\n",
       "       19541.736169, 19541.787662, 19541.840868, 19541.928947, 19541.986806,\n",
       "       19542.082095, 19542.197083, 19542.269178, 19542.327222, 19542.437859,\n",
       "       19542.473819, 19542.51412 , 19542.577558, 19542.710567, 19542.774028,\n",
       "       19542.938229, 19543.07853 , 19543.317581, 19543.677072, 19543.796586,\n",
       "       19543.979329, 19544.197894, 19544.332951, 19544.520625, 19544.59015 ,\n",
       "       19544.723194, 19544.790451, 19544.859387, 19544.930093, 19544.994329,\n",
       "       19545.050799, 19546.403819, 19546.567083, 19546.748472, 19546.874803,\n",
       "       19547.022535, 19547.277141, 19547.399352, 19547.555579, 19547.607836,\n",
       "       19547.774896, 19548.04515 , 19548.220081, 19548.414502, 19548.610602,\n",
       "       19548.814884, 19548.957963, 19549.110752, 19550.657049, 19550.847269,\n",
       "       19551.140046, 19551.293935, 19551.49713 , 19551.75515 , 19551.926505,\n",
       "       19552.1639  , 19552.304201, 19552.599931, 19552.722535, 19552.892812,\n",
       "       19553.105741, 19553.131713, 19553.160104, 19553.188113, 19553.254549,\n",
       "       19553.287928, 19553.318866, 19553.347245, 19553.372951, 19553.399711,\n",
       "       19553.423588, 19553.445903, 19553.470787, 19553.491713, 19553.522326,\n",
       "       19553.557488, 19553.627211, 19553.674236, 19553.775625, 19553.81853 ,\n",
       "       19553.862975, 19553.907928, 19553.953171, 19553.997431, 19554.076227,\n",
       "       19554.120301, 19554.165544, 19554.206042, 19554.242813, 19554.282801,\n",
       "       19554.80978 , 19554.966898, 19555.193183, 19555.378507, 19555.460081,\n",
       "       19555.716516, 19555.898403, 19556.084456, 19556.221991, 19622.241875,\n",
       "       19622.385312, 19622.557604, 19622.721644, 19622.900382, 19623.068241,\n",
       "       19623.627963, 19623.788542, 19623.976667, 19624.169074, 19624.346782,\n",
       "       19624.572222, 19624.738333, 19624.962894, 19625.203206, 19625.368252,\n",
       "       19625.594826, 19625.764468, 19626.034213, 19626.241574, 19626.418796,\n",
       "       19626.599097, 19626.724132, 19626.909977, 19627.127951, 19630.760093,\n",
       "       19630.983299, 19631.246273, 19631.441713, 19631.660127, 19631.848692,\n",
       "       19632.011898, 19632.297894, 19632.499282, 19632.700764, 19632.935243,\n",
       "       19633.131875, 19633.395799, 19633.728692, 19633.933576, 19634.109954,\n",
       "       19634.417593, 19634.86213 , 19635.105185])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cf69880f-dd96-471c-b887-11a95fc682cb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cf69880f-dd96-471c-b887-11a95fc682cb' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>sea_water_temperature</dd><dt><span>long_name :</span></dt><dd>Sea temperature</dd><dt><span>units :</span></dt><dd>degrees_C</dd><dt><span>valid_min :</span></dt><dd>-2500</dd><dt><span>valid_max :</span></dt><dd>40000</dd><dt><span>ancillary_variables :</span></dt><dd>TEMP_QC TEMP_DM</dd><dt><span>data_mode :</span></dt><dd>D</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'TEMP' (TIME: 158, DEPTH: 2907)>\n",
       "array([[   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
       "       [ 9.189,  9.165,  9.136, ...,    nan,    nan,    nan],\n",
       "       [ 9.132,  9.099,  9.08 , ...,    nan,    nan,    nan],\n",
       "       ...,\n",
       "       [11.98 , 11.981, 11.981, ...,    nan,    nan,    nan],\n",
       "       [12.992, 12.992, 12.992, ...,    nan,    nan,    nan],\n",
       "       [12.903, 12.904, 12.904, ...,    nan,    nan,    nan]])\n",
       "Coordinates:\n",
       "  * TIME     (TIME) float64 1.954e+04 1.954e+04 ... 1.963e+04 1.964e+04\n",
       "Dimensions without coordinates: DEPTH\n",
       "Attributes:\n",
       "    standard_name:        sea_water_temperature\n",
       "    long_name:            Sea temperature\n",
       "    units:                degrees_C\n",
       "    valid_min:            -2500\n",
       "    valid_max:            40000\n",
       "    ancillary_variables:  TEMP_QC TEMP_DM\n",
       "    data_mode:            D"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate a filtered xarray with a single variable for selected Platform\n",
    "\n",
    "align_and_nan=True\n",
    "check_alignment(data_dict, pc, var, align_and_nan, vmin_dict)\n",
    "\n",
    "filtered_xarr = filter_xarr(df_toPlot, \n",
    "                            data_dict, \n",
    "                            platform=pc, \n",
    "                            var=var, \n",
    "                            depth_range=[depth_start,\n",
    "                                         depth_stop_dict[pc]])\n",
    "filtered_xarr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data filtered by BBOX and All Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Platform: GT - Vertical min = 1.0 --> aligning and add nan\n",
      "this is a test\n"
     ]
    },
    {
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       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (DEPTH: 2907, TIME: 158)\n",
       "Coordinates:\n",
       "  * TIME     (TIME) float64 1.954e+04 1.954e+04 ... 1.963e+04 1.964e+04\n",
       "Dimensions without coordinates: DEPTH\n",
       "Data variables:\n",
       "    TEMP     (TIME, DEPTH) float64 nan nan nan nan nan ... nan nan nan nan nan\n",
       "    PRES     (TIME, DEPTH) float32 nan nan nan nan nan ... nan nan nan nan nan\n",
       "Attributes: (12/51)\n",
       "    title:                          Arctic Ocean - In Situ Observation Copern...\n",
       "    qc_manual:                      Recommendations for in-situ data Near Rea...\n",
       "    contact:                        cmems-service@imr.no\n",
       "    format_version:                 1.4\n",
       "    distribution_statement:         These data follow Copernicus standards; t...\n",
       "    citation:                       These data were collected and made freely...\n",
       "    ...                             ...\n",
       "    last_latitude_observation:      57.690\n",
       "    geospatial_lon_min:             -9.520\n",
       "    geospatial_lon_max:             35.016\n",
       "    last_longitude_observation:     -0.515\n",
       "    geospatial_vertical_min:        1.000\n",
       "    geospatial_vertical_max:        2962.000</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-a7348fa3-0763-4b29-b627-46d9a71f5137' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a7348fa3-0763-4b29-b627-46d9a71f5137' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>DEPTH</span>: 2907</li><li><span class='xr-has-index'>TIME</span>: 158</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d68fefbb-a7d3-4ca6-824e-e28513a457de' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d68fefbb-a7d3-4ca6-824e-e28513a457de' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>TIME</span></div><div class='xr-var-dims'>(TIME)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.954e+04 1.954e+04 ... 1.964e+04</div><input id='attrs-e05cff87-8127-4b3a-aaa2-23f10c744eb8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e05cff87-8127-4b3a-aaa2-23f10c744eb8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db34028c-396f-4b8b-be8e-7740d9b11127' class='xr-var-data-in' type='checkbox'><label for='data-db34028c-396f-4b8b-be8e-7740d9b11127' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>units :</span></dt><dd>days since 1950-01-01T00:00:00Z</dd><dt><span>valid_min :</span></dt><dd>-90000.0</dd><dt><span>valid_max :</span></dt><dd>90000.0</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>QC_indicator :</span></dt><dd>1</dd><dt><span>uncertainty :</span></dt><dd></dd><dt><span>comment :</span></dt><dd></dd><dt><span>ancillary_variables :</span></dt><dd>TIME_QC</dd><dt><span>calendar :</span></dt><dd>standard</dd></dl></div><div class='xr-var-data'><pre>array([19540.623079, 19541.126748, 19541.140532, 19541.160231, 19541.193125,\n",
       "       19541.22309 , 19541.267199, 19541.297604, 19541.329109, 19541.423391,\n",
       "       19541.447384, 19541.471505, 19541.484294, 19541.590764, 19541.690312,\n",
       "       19541.736169, 19541.787662, 19541.840868, 19541.928947, 19541.986806,\n",
       "       19542.082095, 19542.197083, 19542.269178, 19542.327222, 19542.437859,\n",
       "       19542.473819, 19542.51412 , 19542.577558, 19542.710567, 19542.774028,\n",
       "       19542.938229, 19543.07853 , 19543.317581, 19543.677072, 19543.796586,\n",
       "       19543.979329, 19544.197894, 19544.332951, 19544.520625, 19544.59015 ,\n",
       "       19544.723194, 19544.790451, 19544.859387, 19544.930093, 19544.994329,\n",
       "       19545.050799, 19546.403819, 19546.567083, 19546.748472, 19546.874803,\n",
       "       19547.022535, 19547.277141, 19547.399352, 19547.555579, 19547.607836,\n",
       "       19547.774896, 19548.04515 , 19548.220081, 19548.414502, 19548.610602,\n",
       "       19548.814884, 19548.957963, 19549.110752, 19550.657049, 19550.847269,\n",
       "       19551.140046, 19551.293935, 19551.49713 , 19551.75515 , 19551.926505,\n",
       "       19552.1639  , 19552.304201, 19552.599931, 19552.722535, 19552.892812,\n",
       "       19553.105741, 19553.131713, 19553.160104, 19553.188113, 19553.254549,\n",
       "       19553.287928, 19553.318866, 19553.347245, 19553.372951, 19553.399711,\n",
       "       19553.423588, 19553.445903, 19553.470787, 19553.491713, 19553.522326,\n",
       "       19553.557488, 19553.627211, 19553.674236, 19553.775625, 19553.81853 ,\n",
       "       19553.862975, 19553.907928, 19553.953171, 19553.997431, 19554.076227,\n",
       "       19554.120301, 19554.165544, 19554.206042, 19554.242813, 19554.282801,\n",
       "       19554.80978 , 19554.966898, 19555.193183, 19555.378507, 19555.460081,\n",
       "       19555.716516, 19555.898403, 19556.084456, 19556.221991, 19622.241875,\n",
       "       19622.385312, 19622.557604, 19622.721644, 19622.900382, 19623.068241,\n",
       "       19623.627963, 19623.788542, 19623.976667, 19624.169074, 19624.346782,\n",
       "       19624.572222, 19624.738333, 19624.962894, 19625.203206, 19625.368252,\n",
       "       19625.594826, 19625.764468, 19626.034213, 19626.241574, 19626.418796,\n",
       "       19626.599097, 19626.724132, 19626.909977, 19627.127951, 19630.760093,\n",
       "       19630.983299, 19631.246273, 19631.441713, 19631.660127, 19631.848692,\n",
       "       19632.011898, 19632.297894, 19632.499282, 19632.700764, 19632.935243,\n",
       "       19633.131875, 19633.395799, 19633.728692, 19633.933576, 19634.109954,\n",
       "       19634.417593, 19634.86213 , 19635.105185])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ad62a1f9-cbc6-4dd3-bcf7-dfc71d2fc0e9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ad62a1f9-cbc6-4dd3-bcf7-dfc71d2fc0e9' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>TEMP</span></div><div class='xr-var-dims'>(TIME, DEPTH)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-08aca542-904b-4c93-ab7a-42a946f3bc33' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-08aca542-904b-4c93-ab7a-42a946f3bc33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-454b20fc-6be8-4df0-9793-bece2d5c2d39' class='xr-var-data-in' type='checkbox'><label for='data-454b20fc-6be8-4df0-9793-bece2d5c2d39' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>sea_water_temperature</dd><dt><span>long_name :</span></dt><dd>Sea temperature</dd><dt><span>units :</span></dt><dd>degrees_C</dd><dt><span>valid_min :</span></dt><dd>-2500</dd><dt><span>valid_max :</span></dt><dd>40000</dd><dt><span>ancillary_variables :</span></dt><dd>TEMP_QC TEMP_DM</dd><dt><span>data_mode :</span></dt><dd>D</dd></dl></div><div class='xr-var-data'><pre>array([[   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
       "       [ 9.189,  9.165,  9.136, ...,    nan,    nan,    nan],\n",
       "       [ 9.132,  9.099,  9.08 , ...,    nan,    nan,    nan],\n",
       "       ...,\n",
       "       [11.98 , 11.981, 11.981, ...,    nan,    nan,    nan],\n",
       "       [12.992, 12.992, 12.992, ...,    nan,    nan,    nan],\n",
       "       [12.903, 12.904, 12.904, ...,    nan,    nan,    nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PRES</span></div><div class='xr-var-dims'>(TIME, DEPTH)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-de39533b-ea90-4cf0-8cc1-44b2675a98a2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de39533b-ea90-4cf0-8cc1-44b2675a98a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ecf537ac-d723-49b7-a688-8a80b4a4e671' class='xr-var-data-in' type='checkbox'><label for='data-ecf537ac-d723-49b7-a688-8a80b4a4e671' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>uncertainty :</span></dt><dd> </dd><dt><span>comment :</span></dt><dd> </dd><dt><span>ancillary_variables :</span></dt><dd>PRES_QC</dd><dt><span>standard_name :</span></dt><dd>sea_water_pressure</dd><dt><span>long_name :</span></dt><dd>Sea pressure</dd><dt><span>units :</span></dt><dd>dbar</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>data_mode :</span></dt><dd>D</dd></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [49., 50., 51., ..., nan, nan, nan],\n",
       "       [49., 50., 51., ..., nan, nan, nan],\n",
       "       ...,\n",
       "       [50., 51., 52., ..., nan, nan, nan],\n",
       "       [50., 51., 52., ..., nan, nan, nan],\n",
       "       [51., 52., 53., ..., nan, nan, nan]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-99957029-22a3-4e0e-b3f0-0c6e1326a1f3' class='xr-section-summary-in' type='checkbox'  ><label for='section-99957029-22a3-4e0e-b3f0-0c6e1326a1f3' class='xr-section-summary' >Attributes: <span>(51)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>Arctic Ocean - In Situ Observation Copernicus</dd><dt><span>qc_manual :</span></dt><dd>Recommendations for in-situ data Near Real Time Quality Control https://doi.org/10.13155/36230</dd><dt><span>contact :</span></dt><dd>cmems-service@imr.no</dd><dt><span>format_version :</span></dt><dd>1.4</dd><dt><span>distribution_statement :</span></dt><dd>These data follow Copernicus standards; they are public and free of charge. User assumes all risk for use of data. User must display citation in any publication or product using data. User must contact PI prior to any commercial use of data.</dd><dt><span>citation :</span></dt><dd>These data were collected and made freely available by the Copernicus project and the programs that contribute to it </dd><dt><span>naming_authority :</span></dt><dd>Copernicus Marine In Situ</dd><dt><span>data_assembly_center :</span></dt><dd>IMR</dd><dt><span>update_interval :</span></dt><dd>void</dd><dt><span>area :</span></dt><dd>Arctic Ocean</dd><dt><span>author :</span></dt><dd></dd><dt><span>Conventions :</span></dt><dd>CF-1.6 Copernicus-InSituTAC-FormatManual-1.41 Copernicus-InSituTAC-SRD-1.5 Copernicus-InSituTAC-ParametersList-3.2.0</dd><dt><span>data_mode :</span></dt><dd>D</dd><dt><span>comment :</span></dt><dd></dd><dt><span>history :</span></dt><dd>\\0122021-04-12 07:22:38 GMT Hyrax-1.14.0 http://opendap1.nodc.no:80/opendap/physics/point/yearly/58GT_CTD_2003.nc.nc4?</dd><dt><span>references :</span></dt><dd>http://marine.copernicus.eu http://www.marineinsitu.eu</dd><dt><span>netcdf_version :</span></dt><dd>netCDF-4 classic model</dd><dt><span>quality_index :</span></dt><dd>A</dd><dt><span>quality_control_indicator :</span></dt><dd>0</dd><dt><span>doi :</span></dt><dd></dd><dt><span>summary :</span></dt><dd></dd><dt><span>wmo_platform_code :</span></dt><dd></dd><dt><span>ices_platform_code :</span></dt><dd></dd><dt><span>site_code :</span></dt><dd></dd><dt><span>bottom_depth :</span></dt><dd></dd><dt><span>wmo_inst_type :</span></dt><dd></dd><dt><span>platform_code :</span></dt><dd>58GT</dd><dt><span>platform_name :</span></dt><dd>Sarsen</dd><dt><span>institution :</span></dt><dd>Institute of Marine Research (Norway)</dd><dt><span>institution_edmo_code :</span></dt><dd>1351</dd><dt><span>source :</span></dt><dd>research vessel</dd><dt><span>institution_references :</span></dt><dd>http://www.imr.no</dd><dt><span>institution_country :</span></dt><dd>Norway</dd><dt><span>source_platform_category_code :</span></dt><dd>31</dd><dt><span>pi_name :</span></dt><dd></dd><dt><span>data_type :</span></dt><dd>OceanSITES vertical profile</dd><dt><span>cdm_data_type :</span></dt><dd>profile</dd><dt><span>instrument :</span></dt><dd>CTD Seabird</dd><dt><span>id :</span></dt><dd>58GT_CTD_2003</dd><dt><span>time_coverage_start :</span></dt><dd>2003-01-19T09:59:03Z</dd><dt><span>time_coverage_end :</span></dt><dd>2003-10-05T02:31:28Z</dd><dt><span>last_date_observation :</span></dt><dd>2003-10-05T02:31:28Z</dd><dt><span>date_update :</span></dt><dd>2021-03-23T08:36:42Z</dd><dt><span>geospatial_lat_min :</span></dt><dd>56.667</dd><dt><span>geospatial_lat_max :</span></dt><dd>78.655</dd><dt><span>last_latitude_observation :</span></dt><dd>57.690</dd><dt><span>geospatial_lon_min :</span></dt><dd>-9.520</dd><dt><span>geospatial_lon_max :</span></dt><dd>35.016</dd><dt><span>last_longitude_observation :</span></dt><dd>-0.515</dd><dt><span>geospatial_vertical_min :</span></dt><dd>1.000</dd><dt><span>geospatial_vertical_max :</span></dt><dd>2962.000</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (DEPTH: 2907, TIME: 158)\n",
       "Coordinates:\n",
       "  * TIME     (TIME) float64 1.954e+04 1.954e+04 ... 1.963e+04 1.964e+04\n",
       "Dimensions without coordinates: DEPTH\n",
       "Data variables:\n",
       "    TEMP     (TIME, DEPTH) float64 nan nan nan nan nan ... nan nan nan nan nan\n",
       "    PRES     (TIME, DEPTH) float32 nan nan nan nan nan ... nan nan nan nan nan\n",
       "Attributes: (12/51)\n",
       "    title:                          Arctic Ocean - In Situ Observation Copern...\n",
       "    qc_manual:                      Recommendations for in-situ data Near Rea...\n",
       "    contact:                        cmems-service@imr.no\n",
       "    format_version:                 1.4\n",
       "    distribution_statement:         These data follow Copernicus standards; t...\n",
       "    citation:                       These data were collected and made freely...\n",
       "    ...                             ...\n",
       "    last_latitude_observation:      57.690\n",
       "    geospatial_lon_min:             -9.520\n",
       "    geospatial_lon_max:             35.016\n",
       "    last_longitude_observation:     -0.515\n",
       "    geospatial_vertical_min:        1.000\n",
       "    geospatial_vertical_max:        2962.000"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate a filtered xarray with all variables for selected Platform\n",
    "for var in vars_sel: \n",
    "\n",
    "    align_and_nan=True\n",
    "    check_alignment(data_dict, pc, var, align_and_nan, vmin_dict)   \n",
    "\n",
    "filtered_xarr = filter_xarr(df_toPlot, \n",
    "                            data_dict, \n",
    "                            platform=pc, \n",
    "                            var=vars_sel, \n",
    "                            depth_range=[depth_start,\n",
    "                                         depth_stop_dict[pc]])\n",
    "filtered_xarr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reference Plots\n",
    "The reference plots are generated for the available **variables** of the **filtered** xarrays. On the y-axis is shown the TIME of the measurement (in *float* format, which needs to be converted to *datetime* format), and on the x-axis is the DEPTH of the measurement."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting individual Variables per individual Platform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are multiple VARs available from the applied filtered: ['TEMP', 'PRES']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "try:\n",
    "    var = filtered_xarr.name\n",
    "    print(f\"There's only one VAR available from the applied filtered: {var}\")\n",
    "    \n",
    "    title = f'Platform={pc}, Var={var} (Individual Platform)\\nFilter: Year={year}, {bbox_key}, Depth Range {depth_start}-{depth_stop_dict[pc]}\\nSel/All={sel_outof_all}'\n",
    "    \n",
    "    plotFilteredVar(filtered_xarr, title=title)\n",
    "    \n",
    "except:\n",
    "    myvars = list(filtered_xarr.keys())\n",
    "    print(f\"There are multiple VARs available from the applied filtered: {myvars}\")\n",
    "    \n",
    "    for var in vars_sel:\n",
    "        \n",
    "        title = f'Platform={pc}, Var={var} (Individual Platform)\\nFilter: Year={year}, {bbox_key}, Depth Range {depth_start}-{depth_stop_dict[pc]}\\nSel/All={sel_outof_all}'\n",
    "\n",
    "        plotFilteredVar(filtered_xarr[var], title=title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting individual Variables across aggregated Platforms\n",
    "Build a dictionary that stores, for each platform and variable combination, the data sliced using the same DEPTH dimension in such a way to generate datasets that can be combined / merged."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Generate **filtered_xarr** for all Platforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Platform: AA - Vertical min = 0.0\n",
      "Platform: AA - Vertical min = 0.0\n",
      "Platform: GS - Vertical min = 1.0 --> aligning and add nan\n",
      "this is a test\n",
      "Platform: GS - Vertical min = 1.0 --> aligning and add nan\n",
      "this is a test\n"
     ]
    }
   ],
   "source": [
    "# Generate a filtered xarray with all variables for selected Platform, for a certain DEPTH range\n",
    "filtered_xarr_dict = {}\n",
    "\n",
    "for pc in df_toPlot['Platform'].unique():\n",
    "    \n",
    "    for var in vars_sel: \n",
    "\n",
    "        align_and_nan=True\n",
    "        check_alignment(data_dict, pc, var, align_and_nan, vmin_dict)   \n",
    "        \n",
    "    filtered_xarr_dict[pc] = filter_xarr(df_toPlot, \n",
    "                                         data_dict, \n",
    "                                         platform=pc, \n",
    "                                         var=vars_sel, \n",
    "                                         depth_range=[depth_start,\n",
    "                                                      depth_stop_dict[pc]])\n",
    "    \n",
    "# filtered_xarr_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PC AA\tFiltered Dims: TIME=357, DEPTH=2760\n",
      "PC GS\tFiltered Dims: TIME=49, DEPTH=3634\n",
      "PC GT\tFiltered Dims: TIME=158, DEPTH=2907\n"
     ]
    }
   ],
   "source": [
    "data_var_dict = {}\n",
    "\n",
    "for pc in df_toPlot['Platform'].unique(): \n",
    "    \n",
    "    data_var_dict[pc] = {}\n",
    "    data = filtered_xarr_dict[pc]\n",
    "    \n",
    "    print(f'PC {pc}\\tFiltered Dims: TIME={data.dims[\"TIME\"]}, DEPTH={data.dims[\"DEPTH\"]}')\n",
    "\n",
    "    for var in vars_sel:\n",
    "        data_var_dict[pc][var] = filtered_xarr_dict[pc][var]\n",
    "\n",
    "# display(data_var_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Aggregation\n",
    "Two xarray datasets can be merged if they have the same structure, i.e. dimensions. Here we use two aggregation methods that allow doing that, in two different ways: using the **min_depth_elements** and **max_depth_elements**, which are defined as follows: \n",
    "* **min_depth_elements** = least common denominator for the DEPTH dimension found amongst the elements of the selected platforms\n",
    "* **max_depth_elements** = maximum value of the DEPTH dimension found amongst the elements of the selected platforms \n",
    "\n",
    "The two aggregation methods are:\n",
    "* Aggregation with **min_depth_elements**: data are aggregated using the minimum dimension of depth. The xarrays of those platforms that have a larger depth dimension will be truncated to the common minimum dimension.       \n",
    "* Aggregation with **max_depth_elements**: data are aggregated using the maximum dimension of depth. The xarrays of those platforms that have a lower depth dimension will have their dimension increased to the common maximum dimension and filled with nans."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P.Code (TIME, DEPTH)\n",
      "AA (357, 2760)\n",
      "GS (49, 3634)\n",
      "GT (158, 2907)\n",
      "max_depth_elements: 3634\n",
      "min_depth_elements: 2760\n"
     ]
    }
   ],
   "source": [
    "print(f'P.Code (TIME, DEPTH)')\n",
    "[print(k, data_var_dict[k][\"TEMP\"].shape) for k in data_var_dict.keys()]\n",
    "\n",
    "# Find maximum and minimum DEPTH dimension among the platforms\n",
    "max_depth_elements = max([data_var_dict[k]['TEMP'].shape[1] for k in data_var_dict.keys()]); print('max_depth_elements:', max_depth_elements)\n",
    "min_depth_elements = min([data_var_dict[k]['TEMP'].shape[1] for k in data_var_dict.keys()]); print('min_depth_elements:', min_depth_elements)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NB**: if *min_depth_elements* and *max_depth_elements* are equal to each other, either aggregation methods (with min_depth_elements or with max_depht_elements) will lead to the same result. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Aggregate with **min_depth_elements**\n",
    "Use minimum DEPTH across the platforms' DEPTHs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reshaping using minimum DAPTH dimension (2760):\n",
      "(357, 2760) --> (357, 2760)\n",
      "(357, 2760) --> (357, 2760)\n",
      "(49, 3634) --> (49, 2760)\n",
      "(49, 3634) --> (49, 2760)\n",
      "(158, 2907) --> (158, 2760)\n",
      "(158, 2907) --> (158, 2760)\n"
     ]
    }
   ],
   "source": [
    "# Use min_depth_elements across available platforms: Reduce size to the minimum dimension \n",
    "print(f'Reshaping using minimum DAPTH dimension ({min_depth_elements}):')\n",
    "\n",
    "reshaped_dict = {}\n",
    "\n",
    "for pc in data_var_dict.keys():\n",
    "    reshaped_dict[pc] = {}\n",
    "    \n",
    "    for var in vars_sel:\n",
    "        \n",
    "        data = data_var_dict[pc][var].isel(DEPTH=slice(0,min_depth_elements)); #display(data_sel)\n",
    "        \n",
    "        print(f'{data_var_dict[pc][var].shape} --> {data.shape}')\n",
    "        \n",
    "        reshaped_dict[pc][var] = data\n",
    "        \n",
    "# reshaped_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Aggregate with **max_depth_elements**\n",
    "use maximum DEPTH across all platforms' DEPTHs, and fill empty values with nans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reshaping using maximum DAPTH dimension (3634):\n",
      "(357, 2760) --> (357, 3634)\n",
      "(357, 2760) --> (357, 3634)\n",
      "(49, 3634) --> (49, 3634)\n",
      "(49, 3634) --> (49, 3634)\n",
      "(158, 2907) --> (158, 3634)\n",
      "(158, 2907) --> (158, 3634)\n"
     ]
    }
   ],
   "source": [
    "print(f'Reshaping using maximum DAPTH dimension ({max_depth_elements}):')\n",
    "\n",
    "reshaped_dict = {}\n",
    "\n",
    "for pc in data_var_dict.keys():\n",
    "    reshaped_dict[pc] = {}\n",
    "    \n",
    "    for var in vars_sel:\n",
    "        \n",
    "        arr = data_var_dict[pc][var]\n",
    "        \n",
    "        # generate an empty array of nans, to then concatenate to the existing data array, to ensure it matches the desired new dimension DEPTH \n",
    "        fill = np.empty((arr.shape[0], max_depth_elements-arr.shape[1]), dtype=arr.dtype); fill[:] = np.nan\n",
    "        newarr_data = np.concatenate((arr.data,fill), axis=1)\n",
    "        print(f'{arr.shape} --> {newarr_data.shape}')\n",
    "        \n",
    "        # Now create the xarray.DataArray\n",
    "        newarr = newXDA(arr, newarr_data, var)\n",
    "        \n",
    "        reshaped_dict[pc][var] = newarr\n",
    "\n",
    "# reshaped_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Plotting Aggregate Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Combine arrays across platforms, for each variable\n",
    "merged_arr = {}\n",
    "\n",
    "for var in vars_sel:\n",
    "    \n",
    "    merged_arr[var] = xr.merge([reshaped_dict[pc][var] for pc in reshaped_dict.keys()])  \n",
    "    \n",
    "#     print(var, type(merged_arr[var]), merged_arr[var][var].shape) # Note you need double 'var' because the first is the key of teh dictionary, the second is the name of the var to plot\n",
    "#     display(merged_arr[var])\n",
    "    \n",
    "    title = f'Var={var} (Merged Platforms)\\nFilter: Year={year}, {bbox_key}, Depth Range {depth_start}-{depth_stop}\\nSel/All={sel_outof_all}'\n",
    "\n",
    "    plotVar_MergedPlatforms(merged_arr[var], var, title=title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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