Test1 DAP - OPeNDAP Xarray use cases.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CTD 2003 Subset - Online retrieval optimized for applications\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": {
    "collapsed": "true"
   },
   "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": {},
   "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": "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": {
    "collapsed": "true"
   },
   "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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": "true"
   },
   "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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       "      <td>AA</td>\n",
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       "      <th>681</th>\n",
       "      <td>10.3622</td>\n",
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       "      <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",
       "      <td>63.587700</td>\n",
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       "      <td>AA</td>\n",
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       "      <th>683</th>\n",
       "      <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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     "name": "stdout",
     "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",
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       "      <td>2003-12-12 17:34:18</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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     "name": "stdout",
     "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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       "      <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",
       "      <td>68.175697</td>\n",
       "      <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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     "metadata": {},
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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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       "    <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",
       "    </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"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Merged dataframe with all platforms. Total of 3209 measurement locations\n"
     ]
    },
    {
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       "</style>\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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\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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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.4483</td>\n",
       "      <td>60.748299</td>\n",
       "      <td>2003-01-07 06:05:08</td>\n",
       "      <td>AA</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.2853</td>\n",
       "      <td>60.751499</td>\n",
       "      <td>2003-01-07 08:34:00</td>\n",
       "      <td>AA</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <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",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <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",
       "      <td>3204</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",
       "      <td>3205</td>\n",
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       "    <tr>\n",
       "      <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",
       "      <td>3206</td>\n",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <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",
       "</table>\n",
       "<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() \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) \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": {
    "collapsed": "true"
   },
   "source": [
    "### Plot 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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       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
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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",
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      "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 = $(document.getElementById(\"1002\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
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       "\n",
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       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1004"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh.plotting import figure, output_file, show, ColumnDataSource\n",
    "from bokeh.models import HoverTool\n",
    "from bokeh.io import output_notebook\n",
    "\n",
    "output_notebook() # necessary to show the plot \n",
    "\n",
    "hover = HoverTool(\n",
    "        tooltips=[\n",
    "            (\"index\", \"$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\n",
    "* filter by customised time range "
   ]
  },
  {
   "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": {
      "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>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": {
      "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>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": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        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>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>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"
     ]
    },
    {
     "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>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>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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <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",
       "    </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>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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79 rows × 5 columns</p>\n",
       "</div>"
      ],
      "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": {},
     "output_type": "display_data"
    }
   ],
   "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",
    "# Print index\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 selected Data (Variables within DEPTH range)\n",
    "\n",
    "This section enables accessing data of **only selected variables** and **within a specified DEPTH range**, to avoid fetching unnecessary data and minimise data volume.\n",
    "\n",
    "The list of variables need to be specified in the *vars_sel* variable. 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 \n",
    "\n",
    "The depth range must also be defined. For some limitations to the DAP syntax, at least one range boundary needs to correspond to one of the two extremes. For example, in a data array of 100 elements starting from 0 to 99, the following scenarios are possible:\n",
    "* select the first 20 elements, corresponding to the values range 0 - 19 --> ```[first:1:intermediate] (eg [0:1:19])``` work\n",
    "* select the last 20 elements, corresponsing to the values range 80 - 99 --> ```[intermediate:1:last] (eg [80:1:99])``` work\n",
    "* select the intermediate 60-80 elements, corresponsing to the values range 60 - 79 --> ```[intermediate_1:1:intermediate_2] (eg [80:1:89])``` does NOT work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Range 0-20 Test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define Selected Variables (*vars_sel*)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Available variables\n",
    "vars_main = ['PRES', 'TEMP', 'PSAL', 'CNDC'] \n",
    "\n",
    "# Define the selection of variables to use for the analysis (PS use brakets also with only one var, eg ['TEMP'])\n",
    "vars_sel = ['TEMP', 'PRES']# , 'PSAL', 'CNDC'] \n",
    "assert all([elem in vars_main for elem in vars_sel])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create **data_dict** dictionary \n",
    "Once variables are defined, the DEPTH range must be defined. \n",
    "\n",
    "**INSERT BELOW** the desired DEPTH range (in meters) in the two fields of hte following cell, noting that: \n",
    "* ***metadata[pc]['depth_m_v1']***: either this is equal to the lower bound (ie index=0)\n",
    "* ***metadata[pc]['depth_m_v2']***: or is equal to the upper bound (ie index=-1)\n",
    "\n",
    "Once the variables and DEPTH range are defined, data and their attributes are read iteratively for each platform, 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": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'depth_abs_v1': 0,\n",
      " 'depth_abs_v2': 2809,\n",
      " 'depth_m_v1': 0,\n",
      " 'depth_m_v2': 20,\n",
      " 'depth_newindex4xr_v1': 0,\n",
      " 'depth_newindex4xr_v2': 20,\n",
      " 'depth_newindex_v1': 0,\n",
      " 'depth_newindex_v2': 20,\n",
      " 'vmin': 0}\n",
      "AA DEPTH range of interest (adjusted with vmin): 0 - 20\n",
      "Platform AA URL: 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],TEMP[0:1:683][0:1:20],PRES[0:1:683][0:1:20]\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",
      "{'depth_abs_v1': 0,\n",
      " 'depth_abs_v2': 3683,\n",
      " 'depth_m_v1': 0,\n",
      " 'depth_m_v2': 20,\n",
      " 'depth_newindex4xr_v1': 0,\n",
      " 'depth_newindex4xr_v2': 20,\n",
      " 'depth_newindex_v1': 0,\n",
      " 'depth_newindex_v2': 20,\n",
      " 'vmin': 1}\n",
      "GS DEPTH range of interest (adjusted with vmin): 0 - 20\n",
      "Platform GS URL: 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],TEMP[0:1:404][0:1:20],PRES[0:1:404][0:1:20]\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",
      "{'depth_abs_v1': 0,\n",
      " 'depth_abs_v2': 2956,\n",
      " 'depth_m_v1': 0,\n",
      " 'depth_m_v2': 20,\n",
      " 'depth_newindex4xr_v1': 0,\n",
      " 'depth_newindex4xr_v2': 20,\n",
      " 'depth_newindex_v1': 0,\n",
      " 'depth_newindex_v2': 20,\n",
      " 'vmin': 1}\n",
      "GT DEPTH range of interest (adjusted with vmin): 0 - 20\n",
      "Platform GT URL: 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],TEMP[0:1:990][0:1:20],PRES[0:1:990][0:1:20]\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",
      "{'depth_abs_v1': 0,\n",
      " 'depth_abs_v2': 789,\n",
      " 'depth_m_v1': 0,\n",
      " 'depth_m_v2': 20,\n",
      " 'depth_newindex4xr_v1': 0,\n",
      " 'depth_newindex4xr_v2': 20,\n",
      " 'depth_newindex_v1': 0,\n",
      " 'depth_newindex_v2': 20,\n",
      " 'vmin': 1}\n",
      "HJ DEPTH range of interest (adjusted with vmin): 0 - 20\n",
      "Platform HJ URL: 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],TEMP[0:1:178][0:1:20],PRES[0:1:178][0:1:20]\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",
      "{'depth_abs_v1': 0,\n",
      " 'depth_abs_v2': 3763,\n",
      " 'depth_m_v1': 0,\n",
      " 'depth_m_v2': 20,\n",
      " 'depth_newindex4xr_v1': 0,\n",
      " 'depth_newindex4xr_v2': 20,\n",
      " 'depth_newindex_v1': 0,\n",
      " 'depth_newindex_v2': 20,\n",
      " 'vmin': 1}\n",
      "JH DEPTH range of interest (adjusted with vmin): 0 - 20\n",
      "Platform JH URL: 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],TEMP[0:1:949][0:1:20],PRES[0:1:949][0:1:20]\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",
    "metadata = {}\n",
    "\n",
    "for pc in platform_codes:\n",
    "    \n",
    "    metadata[pc] = {}\n",
    "    \n",
    "    v_min = int(float(position_dict[pc]['data_attr'][6]))\n",
    "    metadata[pc]['vmin'] = v_min\n",
    "    metadata[pc]['depth_abs_v1'] = 0 # this is fixed\n",
    "    metadata[pc]['depth_abs_v2'] = pc_dim_dict[pc]['DEPTH'] # this is fixed\n",
    "    \n",
    "    # ==============================================================================\n",
    "    \"\"\"\n",
    "    Define here the DEPTH range of your selection, in meters. Note that either:\n",
    "    - 'depth_m_v1' is equal to the lower bound (ie index=0), or \n",
    "    - 'depth_m_v2' is equal to the upper bound (ie index=-1)\n",
    "    \"\"\"\n",
    "    metadata[pc]['depth_m_v1'] = 0\n",
    "    metadata[pc]['depth_m_v2'] = 20 #pc_dim_dict[pc]['DEPTH'] \n",
    "    # ==============================================================================\n",
    "    \n",
    "    assert metadata[pc]['depth_m_v1'] < metadata[pc]['depth_m_v2'], 'ERROR: the lower bound must be lower than the higher bound' \n",
    "    assert metadata[pc]['depth_m_v1'] == 0 or metadata[pc]['depth_m_v2'] == pc_dim_dict[pc]['DEPTH'], 'ERROR: one of the two values must be equal to one of the lower/upper bounds'\n",
    "    \n",
    "#     print(f'DEPTH range of interest (meters): {metadata[pc][\"depth_m_v1\"]} - {metadata[pc][\"depth_m_v2\"]}')\n",
    "    \n",
    "    # the start and stop values are adjusted based on the vmin value\n",
    "    if metadata[pc]['vmin'] == 1: \n",
    "        if metadata[pc]['depth_m_v1'] == 0: # \n",
    "            metadata[pc]['depth_newindex_v1'] = metadata[pc]['depth_m_v1'] # the same\n",
    "            metadata[pc]['depth_newindex_v2'] = metadata[pc]['depth_m_v2'] # the same, so I have the right size. When I shift and add the nan, I get rid of further element on the right\n",
    "            metadata[pc]['depth_newindex4xr_v2'] = metadata[pc]['depth_m_v2']# - 1\n",
    "            \n",
    "        elif metadata[pc]['depth_m_v1'] != 0: \n",
    "            metadata[pc]['depth_newindex_v1'] = metadata[pc]['depth_m_v1'] - 1 # start one element before\n",
    "            metadata[pc]['depth_newindex_v2'] = metadata[pc]['depth_m_v2'] - 1 # last element is excluded, ie stop one element before. But then I'll have to remoove one element\n",
    "            metadata[pc]['depth_newindex4xr_v2'] = metadata[pc]['depth_m_v2'] - metadata[pc]['depth_m_v1'] - 1 \n",
    "        \n",
    "    else:\n",
    "        metadata[pc]['depth_newindex_v1'] = metadata[pc]['depth_m_v1']\n",
    "        metadata[pc]['depth_newindex_v2'] = metadata[pc]['depth_m_v2']\n",
    "        \n",
    "        if metadata[pc]['depth_m_v1'] == 0: # \n",
    "            metadata[pc]['depth_newindex4xr_v2'] = metadata[pc]['depth_m_v2']\n",
    "\n",
    "        elif metadata[pc]['depth_m_v1'] != 0: \n",
    "            metadata[pc]['depth_newindex4xr_v2'] = metadata[pc]['depth_m_v2'] - metadata[pc]['depth_m_v1']\n",
    "            \n",
    "    metadata[pc]['depth_newindex4xr_v1'] = 0\n",
    "    \n",
    "    pprint.pprint(metadata[pc])\n",
    "    print(f'{pc} DEPTH range of interest (adjusted with vmin): {metadata[pc][\"depth_newindex_v1\"]} - {metadata[pc][\"depth_newindex_v2\"]}')\n",
    "    \n",
    "    fix_lab = f'58{pc}_CTD_{year}' # platform_codes and year are defined at the beginning of the notebook \n",
    "    \n",
    "    # Get coordinates (needed for keeping hte correct structure, and for plotting) \n",
    "    coords_str = getQueryString(pc_dim_dict[pc], keylist = ['TIME', 'LATITUDE', 'LONGITUDE']) # list the coordinates you want\n",
    "    \n",
    "    # Extract TIME and DEPTH dimension for queries \n",
    "    time_dims = getQuery(pc, start=0, stop=pc_dim_dict[pc]['TIME'])\n",
    "    depth_dims = getQuery(pc, start=metadata[pc]['depth_newindex_v1'], stop=metadata[pc]['depth_newindex_v2'])#; print(depth_dims)\n",
    "    \n",
    "    # join TIME and DEPTH for Variables\n",
    "    var_str_ALL = []\n",
    "    for var in vars_sel: var_str_ALL = np.append(var_str_ALL, f'{var}{time_dims}{depth_dims}')\n",
    "    queries_vars = ','.join(var_str_ALL)\n",
    " \n",
    "    # Build url and url with queries (url_q)\n",
    "    url = f'{server_url}{fix_lab}.nc.nc4?{coords_str}' \n",
    "    url_q = f'{url},{queries_vars}'; print(f'Platform {pc} URL:', url_q)\n",
    "        \n",
    "    remote_data, data_attr = fetch_data(url_q)\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": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Show xarray.Dataset of one platform as example\n",
    "# data_dict['AA']['data']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Overview Dataframe with 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": 19,
   "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": 19,
     "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": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AA ['TIME', 'LATITUDE', 'LONGITUDE', 'PRES', 'TEMP']\n",
      "GS ['TIME', 'LATITUDE', 'LONGITUDE', 'PRES', 'TEMP']\n",
      "GT ['TIME', 'LATITUDE', 'LONGITUDE', 'PRES', 'TEMP']\n",
      "HJ ['TIME', 'LATITUDE', 'LONGITUDE', 'PRES', 'TEMP']\n",
      "JH ['TIME', 'LATITUDE', 'LONGITUDE', 'PRES', 'TEMP']\n"
     ]
    }
   ],
   "source": [
    "# Extract / define the variables to use for the analysis\n",
    "for key in data_dict.keys():\n",
    "    print(key, list(data_dict[key]['data'].variables))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define Filtered Data (*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": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BBOX: BBOX=North Sea\n",
      "Selected positions (out of available positions): 564 out of 3209.\n"
     ]
    },
    {
     "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>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": [
    "# 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": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing DEPTH range for analyis:\n",
      " - Pl. AA; DEPTH range of interest: 0 - 20\n",
      " - Pl. GS; DEPTH range of interest: 0 - 20\n",
      " - Pl. GT; DEPTH range of interest: 0 - 20\n",
      " - Pl. HJ; DEPTH range of interest: 0 - 20\n",
      " - Pl. JH; DEPTH range of interest: 0 - 20\n"
     ]
    }
   ],
   "source": [
    "print('Printing DEPTH range for analyis:')\n",
    "assert len(np.unique([metadata[k][\"depth_m_v1\"] for k in metadata.keys()])==1)\n",
    "assert len(np.unique([metadata[k][\"depth_m_v2\"] for k in metadata.keys()])==1)\n",
    "\n",
    "for k in metadata.keys():\n",
    "    print(f' - Pl. {k}; DEPTH range of interest: {metadata[k][\"depth_m_v1\"]} - {metadata[k][\"depth_m_v2\"]}')"
   ]
  },
  {
   "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 Platform Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "pc = 'GT'; assert pc in df_toPlot.Platform.unique(); # platform code"
   ]
  },
  {
   "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": 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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       "\n",
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       "\n",
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       "\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-6d4371f8-d4c6-461f-a442-83ac159a1ea2' class='xr-array-in' type='checkbox' checked><label for='section-6d4371f8-d4c6-461f-a442-83ac159a1ea2' 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-06f66958-a120-4a01-97ee-f72bb9880949' class='xr-section-summary-in' type='checkbox'  checked><label for='section-06f66958-a120-4a01-97ee-f72bb9880949' 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-d4141a60-f561-4894-a1d6-203a3c020784' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d4141a60-f561-4894-a1d6-203a3c020784' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4403e99b-4d97-47c2-ae1e-baeca61cfaf9' class='xr-var-data-in' type='checkbox'><label for='data-4403e99b-4d97-47c2-ae1e-baeca61cfaf9' 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-b6dc1e93-799b-44a0-8b97-9dc4c23316af' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b6dc1e93-799b-44a0-8b97-9dc4c23316af' 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 one single VAR for selected Platform, for a certain DEPTH range\n",
    "\n",
    "if metadata[pc]['depth_m_v1']==0: align_and_nan = True\n",
    "else: align_and_nan = False\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=[metadata[pc]['depth_newindex4xr_v1'],\n",
    "                                         metadata[pc]['depth_newindex4xr_v2']])\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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       "</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: (12/117)\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",
       "    CNDC_QC.conventions:            Copernicus Marine In Situ reference table 2\n",
       "    CNDC_QC.valid_min:              0\n",
       "    CNDC_QC.valid_max:              9\n",
       "    CNDC_QC.flag_values:            [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
       "    CNDC_QC.flag_meanings:          no_qc_performed good_data probably_good_d...\n",
       "    CNDC_QC.long_name:              Electrical conductivity quality flag</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-36d456c7-1f68-4b52-9036-0dd506c1b962' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-36d456c7-1f68-4b52-9036-0dd506c1b962' 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-3aae172c-8f81-412a-8a5f-750e302d1f5c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3aae172c-8f81-412a-8a5f-750e302d1f5c' 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-af9828f9-c8e5-42ef-80e7-9f2a5f1d0960' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-af9828f9-c8e5-42ef-80e7-9f2a5f1d0960' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-114ee97d-5aa5-4a87-8e95-03c1165788cb' class='xr-var-data-in' type='checkbox'><label for='data-114ee97d-5aa5-4a87-8e95-03c1165788cb' 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-6ce34926-96bf-48bb-93fa-1fb489183553' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6ce34926-96bf-48bb-93fa-1fb489183553' 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-0cadb775-20e8-4b6f-b3d4-c692f8ad4b2f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0cadb775-20e8-4b6f-b3d4-c692f8ad4b2f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-87079523-3ace-4655-813d-ba2a5fdd7012' class='xr-var-data-in' type='checkbox'><label for='data-87079523-3ace-4655-813d-ba2a5fdd7012' 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-4177cda7-8160-4d2f-860f-f2d7d1638d5a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4177cda7-8160-4d2f-860f-f2d7d1638d5a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df2bef9d-c2f1-45ed-9e51-ee8ae3317829' class='xr-var-data-in' type='checkbox'><label for='data-df2bef9d-c2f1-45ed-9e51-ee8ae3317829' 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-fc4e24fa-5561-415a-a5eb-115626bc008d' class='xr-section-summary-in' type='checkbox'  ><label for='section-fc4e24fa-5561-415a-a5eb-115626bc008d' class='xr-section-summary' >Attributes: <span>(117)</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><dt><span>TIME_QC._FillValue :</span></dt><dd>-127</dd><dt><span>TIME_QC.conventions :</span></dt><dd>Copernicus Marine In Situ reference table 2</dd><dt><span>TIME_QC.valid_min :</span></dt><dd>0</dd><dt><span>TIME_QC.valid_max :</span></dt><dd>9</dd><dt><span>TIME_QC.flag_values :</span></dt><dd>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]</dd><dt><span>TIME_QC.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>TIME_QC.long_name :</span></dt><dd>Time quality flag</dd><dt><span>POSITION_QC._FillValue :</span></dt><dd>-127</dd><dt><span>POSITION_QC.conventions :</span></dt><dd>Copernicus Marine In Situ reference table 2</dd><dt><span>POSITION_QC.valid_min :</span></dt><dd>0</dd><dt><span>POSITION_QC.valid_max :</span></dt><dd>9</dd><dt><span>POSITION_QC.flag_values :</span></dt><dd>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]</dd><dt><span>POSITION_QC.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>POSITION_QC.long_name :</span></dt><dd>Position quality flag</dd><dt><span>DIRECTION._FillValue :</span></dt><dd> </dd><dt><span>DIRECTION.long_name :</span></dt><dd>Direction of the profiles</dd><dt><span>DIRECTION.conventions :</span></dt><dd>A: ascending profile, D: descending profile</dd><dt><span>DIRECTION.string_length :</span></dt><dd>[991, 992]</dd><dt><span>PRES_QC._FillValue :</span></dt><dd>-127</dd><dt><span>PRES_QC.conventions :</span></dt><dd>Copernicus Marine In Situ reference table 2</dd><dt><span>PRES_QC.valid_min :</span></dt><dd>0</dd><dt><span>PRES_QC.valid_max :</span></dt><dd>9</dd><dt><span>PRES_QC.flag_values :</span></dt><dd>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]</dd><dt><span>PRES_QC.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>PRES_QC.long_name :</span></dt><dd>Sea pressure quality flag</dd><dt><span>PSAL._FillValue :</span></dt><dd>-2147483647</dd><dt><span>PSAL.scale_factor :</span></dt><dd>0.001</dd><dt><span>PSAL.add_offset :</span></dt><dd>0.0</dd><dt><span>PSAL.standard_name :</span></dt><dd>sea_water_practical_salinity</dd><dt><span>PSAL.long_name :</span></dt><dd>Practical salinity</dd><dt><span>PSAL.units :</span></dt><dd>0.001</dd><dt><span>PSAL.valid_min :</span></dt><dd>2000</dd><dt><span>PSAL.valid_max :</span></dt><dd>41000</dd><dt><span>PSAL.ancillary_variables :</span></dt><dd>PSAL_QC PSAL_DM</dd><dt><span>PSAL.data_mode :</span></dt><dd>D</dd><dt><span>CNDC._FillValue :</span></dt><dd>-2147483647</dd><dt><span>CNDC.scale_factor :</span></dt><dd>0.001</dd><dt><span>CNDC.add_offset :</span></dt><dd>0.0</dd><dt><span>CNDC.standard_name :</span></dt><dd>sea_water_electrical_conductivity</dd><dt><span>CNDC.long_name :</span></dt><dd>Electrical conductivity</dd><dt><span>CNDC.units :</span></dt><dd>S m-1</dd><dt><span>CNDC.valid_min :</span></dt><dd>0</dd><dt><span>CNDC.valid_max :</span></dt><dd>50000</dd><dt><span>CNDC.ancillary_variables :</span></dt><dd>CNDC_QC CNDC_DM</dd><dt><span>CNDC.data_mode :</span></dt><dd>D</dd><dt><span>TEMP_QC._FillValue :</span></dt><dd>-127</dd><dt><span>TEMP_QC.conventions :</span></dt><dd>Copernicus Marine In Situ reference table 2</dd><dt><span>TEMP_QC.valid_min :</span></dt><dd>0</dd><dt><span>TEMP_QC.valid_max :</span></dt><dd>9</dd><dt><span>TEMP_QC.flag_values :</span></dt><dd>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]</dd><dt><span>TEMP_QC.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>TEMP_QC.long_name :</span></dt><dd>Sea temperature quality flag</dd><dt><span>PSAL_QC._FillValue :</span></dt><dd>-127</dd><dt><span>PSAL_QC.conventions :</span></dt><dd>Copernicus Marine In Situ reference table 2</dd><dt><span>PSAL_QC.valid_min :</span></dt><dd>0</dd><dt><span>PSAL_QC.valid_max :</span></dt><dd>9</dd><dt><span>PSAL_QC.flag_values :</span></dt><dd>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]</dd><dt><span>PSAL_QC.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>PSAL_QC.long_name :</span></dt><dd>Practical salinity quality flag</dd><dt><span>CNDC_QC._FillValue :</span></dt><dd>-127</dd><dt><span>CNDC_QC.conventions :</span></dt><dd>Copernicus Marine In Situ reference table 2</dd><dt><span>CNDC_QC.valid_min :</span></dt><dd>0</dd><dt><span>CNDC_QC.valid_max :</span></dt><dd>9</dd><dt><span>CNDC_QC.flag_values :</span></dt><dd>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]</dd><dt><span>CNDC_QC.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>CNDC_QC.long_name :</span></dt><dd>Electrical conductivity quality flag</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: (12/117)\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",
       "    CNDC_QC.conventions:            Copernicus Marine In Situ reference table 2\n",
       "    CNDC_QC.valid_min:              0\n",
       "    CNDC_QC.valid_max:              9\n",
       "    CNDC_QC.flag_values:            [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
       "    CNDC_QC.flag_meanings:          no_qc_performed good_data probably_good_d...\n",
       "    CNDC_QC.long_name:              Electrical conductivity quality flag"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate a filtered xarray with all variables for selected Platform, for a certain DEPTH range\n",
    "for var in vars_sel: \n",
    "    \n",
    "    if metadata[pc]['depth_m_v1']==0: align_and_nan = True\n",
    "    else: align_and_nan = False\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=[metadata[pc]['depth_newindex4xr_v1'],\n",
    "                                         metadata[pc]['depth_newindex4xr_v2']])\n",
    "\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": [
    "myvars = list(filtered_xarr.keys()) #filtered_xarr.name\n",
    "\n",
    "if len(myvars) == 1:\n",
    "    var = myvars[0]\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 {metadata[pc][\"depth_m_v1\"]}-{metadata[pc][\"depth_m_v2\"]}\\nSel/All={sel_outof_all}'\n",
    "\n",
    "    plotFilteredVar(filtered_xarr[var], title=title)\n",
    "\n",
    "else:\n",
    "    print(f\"There are multiple VARs available from the applied filtered: {myvars}\")\n",
    "    \n",
    "    for var in myvars:\n",
    "        \n",
    "        title = f'Platform={pc}, Var={var} (Individual Platform)\\nFilter: Year={year}, {bbox_key}, Depth Range {metadata[pc][\"depth_m_v1\"]}-{metadata[pc][\"depth_m_v2\"]}\\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_dict** 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",
    "        if metadata[pc]['depth_m_v1']==0: align_and_nan = True\n",
    "        else: align_and_nan = False\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=[metadata[pc][\"depth_newindex4xr_v1\"],\n",
    "                                                      metadata[pc][\"depth_newindex4xr_v2\"]])\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 DEPTH 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 DEPTH 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 DEPTH 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 DEPTH 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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\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 {metadata[pc][\"depth_m_v1\"]}-{metadata[pc][\"depth_m_v2\"]}\\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 Selected Variables (*vars_sel*)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Available variables\n",
    "vars_main = ['PRES', 'TEMP', 'PSAL', 'CNDC'] \n",
    "\n",
    "# Define the selection of variables to use for the analysis (PS use brakets also with only one var, eg ['TEMP'])\n",
    "vars_sel = ['TEMP', 'PRES'] # 'PSAL', 'CNDC'] # \n",
    "assert all([elem in vars_main for elem in vars_sel])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create **data_dict** dictionary \n",
    "Once variables and depth range are defined, the data and their attributes are read iteratively for each platform, 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)  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Define DEPTH range (INSERT BELOW)** the desired DEPTH range (in meters) in the two fields below, noting that: \n",
    "* ***metadata[pc]['depth_m_v1']***: either this is equal to the lower bound (ie index=0)\n",
    "* ***metadata[pc]['depth_m_v2']***: or is equal to the upper bound (ie index=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'depth_abs_v1': 0,\n",
      " 'depth_abs_v2': 2809,\n",
      " 'depth_m_v1': 50,\n",
      " 'depth_m_v2': 2809,\n",
      " 'depth_newindex4xr_v1': 0,\n",
      " 'depth_newindex4xr_v2': 2759,\n",
      " 'depth_newindex_v1': 50,\n",
      " 'depth_newindex_v2': 2809,\n",
      " 'vmin': 0}\n",
      "AA DEPTH range of interest (adjusted with vmin): 50 - 2809\n",
      "Platform AA URL: 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],TEMP[0:1:683][50:1:2809],PRES[0:1:683][50:1:2809]\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",
      "{'depth_abs_v1': 0,\n",
      " 'depth_abs_v2': 3683,\n",
      " 'depth_m_v1': 50,\n",
      " 'depth_m_v2': 3683,\n",
      " 'depth_newindex4xr_v1': 0,\n",
      " 'depth_newindex4xr_v2': 3632,\n",
      " 'depth_newindex_v1': 49,\n",
      " 'depth_newindex_v2': 3682,\n",
      " 'vmin': 1}\n",
      "GS DEPTH range of interest (adjusted with vmin): 49 - 3682\n",
      "Platform GS URL: 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],TEMP[0:1:404][49:1:3682],PRES[0:1:404][49:1:3682]\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",
      "{'depth_abs_v1': 0,\n",
      " 'depth_abs_v2': 2956,\n",
      " 'depth_m_v1': 50,\n",
      " 'depth_m_v2': 2956,\n",
      " 'depth_newindex4xr_v1': 0,\n",
      " 'depth_newindex4xr_v2': 2905,\n",
      " 'depth_newindex_v1': 49,\n",
      " 'depth_newindex_v2': 2955,\n",
      " 'vmin': 1}\n",
      "GT DEPTH range of interest (adjusted with vmin): 49 - 2955\n",
      "Platform GT URL: 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],TEMP[0:1:990][49:1:2955],PRES[0:1:990][49:1:2955]\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",
      "{'depth_abs_v1': 0,\n",
      " 'depth_abs_v2': 789,\n",
      " 'depth_m_v1': 50,\n",
      " 'depth_m_v2': 789,\n",
      " 'depth_newindex4xr_v1': 0,\n",
      " 'depth_newindex4xr_v2': 738,\n",
      " 'depth_newindex_v1': 49,\n",
      " 'depth_newindex_v2': 788,\n",
      " 'vmin': 1}\n",
      "HJ DEPTH range of interest (adjusted with vmin): 49 - 788\n",
      "Platform HJ URL: 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],TEMP[0:1:178][49:1:788],PRES[0:1:178][49:1:788]\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",
      "{'depth_abs_v1': 0,\n",
      " 'depth_abs_v2': 3763,\n",
      " 'depth_m_v1': 50,\n",
      " 'depth_m_v2': 3763,\n",
      " 'depth_newindex4xr_v1': 0,\n",
      " 'depth_newindex4xr_v2': 3712,\n",
      " 'depth_newindex_v1': 49,\n",
      " 'depth_newindex_v2': 3762,\n",
      " 'vmin': 1}\n",
      "JH DEPTH range of interest (adjusted with vmin): 49 - 3762\n",
      "Platform JH URL: 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],TEMP[0:1:949][49:1:3762],PRES[0:1:949][49:1:3762]\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",
    "metadata = {}\n",
    "\n",
    "for pc in platform_codes:\n",
    "    \n",
    "    metadata[pc] = {}\n",
    "    \n",
    "    v_min = int(float(position_dict[pc]['data_attr'][6]))\n",
    "    metadata[pc]['vmin'] = v_min\n",
    "    metadata[pc]['depth_abs_v1'] = 0 # this is fixed\n",
    "    metadata[pc]['depth_abs_v2'] = pc_dim_dict[pc]['DEPTH'] # this is fixed\n",
    "    \n",
    "    # ==============================================================================\n",
    "    \"\"\"\n",
    "    Define here the DEPTH range of your selection, in meters. Note that either:\n",
    "    - 'depth_m_v1' is equal to the lower bound (ie index=0), or \n",
    "    - 'depth_m_v2' is equal to the upper bound (ie index=-1)\n",
    "    \"\"\"\n",
    "    metadata[pc]['depth_m_v1'] = 50\n",
    "    metadata[pc]['depth_m_v2'] = pc_dim_dict[pc]['DEPTH'] \n",
    "    # ==============================================================================\n",
    "    \n",
    "    assert metadata[pc]['depth_m_v1'] < metadata[pc]['depth_m_v2'], 'ERROR: the lower bound must be lower than the higher bound' \n",
    "    assert metadata[pc]['depth_m_v1'] == 0 or metadata[pc]['depth_m_v2'] == pc_dim_dict[pc]['DEPTH'], 'ERROR: one of the two values must be equal to one of the lower/upper bounds'\n",
    "    \n",
    "#     print(f'DEPTH range of interest (meters): {metadata[pc][\"depth_m_v1\"]} - {metadata[pc][\"depth_m_v2\"]}')\n",
    "    \n",
    "    # the start and stop values are adjusted based on the vmin value\n",
    "    if metadata[pc]['vmin'] == 1: \n",
    "        if metadata[pc]['depth_m_v1'] == 0: # \n",
    "            metadata[pc]['depth_newindex_v1'] = metadata[pc]['depth_m_v1'] # the same\n",
    "            metadata[pc]['depth_newindex_v2'] = metadata[pc]['depth_m_v2'] # the same, so I have the right size. When I shift and add the nan, I get rid of further element on the right\n",
    "            metadata[pc]['depth_newindex4xr_v2'] = metadata[pc]['depth_m_v2'] - 1\n",
    "            \n",
    "        elif metadata[pc]['depth_m_v1'] != 0: \n",
    "            metadata[pc]['depth_newindex_v1'] = metadata[pc]['depth_m_v1'] - 1 # start one element before\n",
    "            metadata[pc]['depth_newindex_v2'] = metadata[pc]['depth_m_v2'] - 1 # last element is excluded, ie stop one element before. But then I'll have to remoove one element\n",
    "            metadata[pc]['depth_newindex4xr_v2'] = metadata[pc]['depth_m_v2'] - metadata[pc]['depth_m_v1'] - 1 \n",
    "        \n",
    "    else:\n",
    "        metadata[pc]['depth_newindex_v1'] = metadata[pc]['depth_m_v1']\n",
    "        metadata[pc]['depth_newindex_v2'] = metadata[pc]['depth_m_v2']\n",
    "        \n",
    "        if metadata[pc]['depth_m_v1'] == 0: # \n",
    "            metadata[pc]['depth_newindex4xr_v2'] = metadata[pc]['depth_m_v2']\n",
    "\n",
    "        elif metadata[pc]['depth_m_v1'] != 0: \n",
    "            metadata[pc]['depth_newindex4xr_v2'] = metadata[pc]['depth_m_v2'] - metadata[pc]['depth_m_v1']\n",
    "            \n",
    "    metadata[pc]['depth_newindex4xr_v1'] = 0\n",
    "    \n",
    "    pprint.pprint(metadata[pc])\n",
    "    print(f'{pc} DEPTH range of interest (adjusted with vmin): {metadata[pc][\"depth_newindex_v1\"]} - {metadata[pc][\"depth_newindex_v2\"]}')\n",
    "    \n",
    "    fix_lab = f'58{pc}_CTD_{year}' # platform_codes and year are defined at the beginning of the notebook \n",
    "    \n",
    "    # Get coordinates (needed for keeping hte correct structure, and for plotting) \n",
    "    coords_str = getQueryString(pc_dim_dict[pc], keylist = ['TIME', 'LATITUDE', 'LONGITUDE']) # list the coordinates you want\n",
    "    \n",
    "    # Extract TIME and DEPTH dimension for queries \n",
    "    time_dims = getQuery(pc, start=0, stop=pc_dim_dict[pc]['TIME'])\n",
    "    depth_dims = getQuery(pc, start=metadata[pc]['depth_newindex_v1'], stop=metadata[pc]['depth_newindex_v2'])#; print(depth_dims)\n",
    "    \n",
    "    # join TIME and DEPTH for Variables\n",
    "    var_str_ALL = []\n",
    "    for var in vars_sel: var_str_ALL = np.append(var_str_ALL, f'{var}{time_dims}{depth_dims}')\n",
    "    queries_vars = ','.join(var_str_ALL)\n",
    " \n",
    "    # Build url and url with queries (url_q)\n",
    "    url = f'{server_url}{fix_lab}.nc.nc4?{coords_str}' \n",
    "    url_q = f'{url},{queries_vars}'; print(f'Platform {pc} URL:', url_q)\n",
    "        \n",
    "    remote_data, data_attr = fetch_data(url_q)\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": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Show xarray.Dataset of one platform as example\n",
    "# data_dict['AA']['data']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Overview Dataframe with 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": 38,
   "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": 38,
     "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": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AA ['TIME', 'LATITUDE', 'LONGITUDE', 'PRES', 'TEMP']\n",
      "GS ['TIME', 'LATITUDE', 'LONGITUDE', 'PRES', 'TEMP']\n",
      "GT ['TIME', 'LATITUDE', 'LONGITUDE', 'PRES', 'TEMP']\n",
      "HJ ['TIME', 'LATITUDE', 'LONGITUDE', 'PRES', 'TEMP']\n",
      "JH ['TIME', 'LATITUDE', 'LONGITUDE', 'PRES', 'TEMP']\n"
     ]
    }
   ],
   "source": [
    "# Extract / define the variables to use for the analysis\n",
    "for key in data_dict.keys():\n",
    "    print(key, list(data_dict[key]['data'].variables))"
   ]
  },
  {
   "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": 40,
   "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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       "      <td>AA</td>\n",
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       "    <tr>\n",
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       "      <td>0.0065</td>\n",
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       "      <td>AA</td>\n",
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       "      <td>GT</td>\n",
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       "      <td>GT</td>\n",
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       "      <td>GT</td>\n",
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       "                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",
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   ],
   "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": "code",
   "execution_count": 41,
   "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": 41,
     "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": [
    "### Selected DEPTH range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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": [
    "print('Printing DEPTH range for analyis:')\n",
    "assert len(np.unique([metadata[k][\"depth_m_v1\"] for k in metadata.keys()])==1)\n",
    "assert len(np.unique([metadata[k][\"depth_m_v2\"] for k in metadata.keys()])==1)\n",
    "\n",
    "for k in metadata.keys():\n",
    "    print(f' - Pl. {k}; DEPTH range of interest: {metadata[k][\"depth_m_v1\"]} - {metadata[k][\"depth_m_v2\"]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define Platform Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "pc = 'GT'; assert pc in df_toPlot.Platform.unique(); # platform code"
   ]
  },
  {
   "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": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define var to plot\n",
    "var = 'TEMP'; assert var in vars_sel # variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Platform: GT - Vertical min = 1.0 --> data has been aligned already\n"
     ]
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       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
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       "\n",
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       "  grid-column: 1;\n",
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       "\n",
       ".xr-var-dims {\n",
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       "\n",
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       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
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       "\n",
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       "  grid-column: 4;\n",
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       "\n",
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       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
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       "\n",
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       ".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",
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       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
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       "\n",
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       "\n",
       ".xr-attrs dd {\n",
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       "  stroke: currentColor;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;TEMP&#x27; (TIME: 158, DEPTH: 2858)&gt;\n",
       "array([[   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
       "       [ 9.165,  9.136,  9.08 , ...,    nan,    nan,    nan],\n",
       "       [ 9.099,  9.08 ,  9.045, ...,    nan,    nan,    nan],\n",
       "       ...,\n",
       "       [11.981, 11.981, 11.981, ...,    nan,    nan,    nan],\n",
       "       [12.992, 12.992, 12.992, ...,    nan,    nan,    nan],\n",
       "       [12.904, 12.904, 12.905, ...,    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>: 2858</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-aa282032-ea1e-4978-a61e-f6e3a633eada' class='xr-array-in' type='checkbox' checked><label for='section-aa282032-ea1e-4978-a61e-f6e3a633eada' 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",
       "       [ 9.165,  9.136,  9.08 , ...,    nan,    nan,    nan],\n",
       "       [ 9.099,  9.08 ,  9.045, ...,    nan,    nan,    nan],\n",
       "       ...,\n",
       "       [11.981, 11.981, 11.981, ...,    nan,    nan,    nan],\n",
       "       [12.992, 12.992, 12.992, ...,    nan,    nan,    nan],\n",
       "       [12.904, 12.904, 12.905, ...,    nan,    nan,    nan]])</pre></div></div></li><li class='xr-section-item'><input id='section-04143cc0-44ad-43b4-9801-a04511152f9d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-04143cc0-44ad-43b4-9801-a04511152f9d' 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-1da8e3d5-020d-4ad4-971a-799543e31660' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1da8e3d5-020d-4ad4-971a-799543e31660' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-56c0fb79-a254-4de1-b535-6075c98c8999' class='xr-var-data-in' type='checkbox'><label for='data-56c0fb79-a254-4de1-b535-6075c98c8999' 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-58b927f2-3fba-46a4-a076-aad5bbece5fd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-58b927f2-3fba-46a4-a076-aad5bbece5fd' 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: 2858)>\n",
       "array([[   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
       "       [ 9.165,  9.136,  9.08 , ...,    nan,    nan,    nan],\n",
       "       [ 9.099,  9.08 ,  9.045, ...,    nan,    nan,    nan],\n",
       "       ...,\n",
       "       [11.981, 11.981, 11.981, ...,    nan,    nan,    nan],\n",
       "       [12.992, 12.992, 12.992, ...,    nan,    nan,    nan],\n",
       "       [12.904, 12.904, 12.905, ...,    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": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate a filtered xarray with one single VAR for selected Platform, for a certain DEPTH range\n",
    "\n",
    "if metadata[pc]['depth_m_v1']==0: align_and_nan = True\n",
    "else: align_and_nan = False\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=[metadata[pc]['depth_newindex4xr_v1'],\n",
    "                                         metadata[pc]['depth_newindex4xr_v2']])\n",
    "filtered_xarr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data filtered by BBOX and All Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Platform: GT - Vertical min = 1.0 --> data has been aligned already\n"
     ]
    },
    {
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       ".xr-section-summary,\n",
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       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
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       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
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       "  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",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\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",
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       "\n",
       ".xr-has-index {\n",
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       "\n",
       ".xr-var-list,\n",
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       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
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       ".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",
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       "\n",
       ".xr-var-name {\n",
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       "\n",
       ".xr-var-dims {\n",
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       "\n",
       ".xr-var-dtype {\n",
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       ".xr-var-preview {\n",
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       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
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       "  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",
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       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
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       "\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",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (DEPTH: 2858, 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: (12/117)\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",
       "    CNDC_QC.conventions:            Copernicus Marine In Situ reference table 2\n",
       "    CNDC_QC.valid_min:              0\n",
       "    CNDC_QC.valid_max:              9\n",
       "    CNDC_QC.flag_values:            [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
       "    CNDC_QC.flag_meanings:          no_qc_performed good_data probably_good_d...\n",
       "    CNDC_QC.long_name:              Electrical conductivity quality flag</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-4d8c5fe5-4fac-4656-8972-8993588ad111' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4d8c5fe5-4fac-4656-8972-8993588ad111' 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>: 2858</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-f24eee25-ef12-4c0e-8b38-61bbafd94877' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f24eee25-ef12-4c0e-8b38-61bbafd94877' 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-b04fd6c1-3db7-4d26-a0b2-c2872949e17f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b04fd6c1-3db7-4d26-a0b2-c2872949e17f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da674a5e-8692-4b45-b451-1d766e3e7bc4' class='xr-var-data-in' type='checkbox'><label for='data-da674a5e-8692-4b45-b451-1d766e3e7bc4' 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-2bb323b3-4ab7-41b6-bcdb-9f3c8df39587' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2bb323b3-4ab7-41b6-bcdb-9f3c8df39587' 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-ee43f05a-1e78-46a0-b686-135e1b027676' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ee43f05a-1e78-46a0-b686-135e1b027676' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f181e2ea-64fc-40bf-ac9b-b024e3a3004e' class='xr-var-data-in' type='checkbox'><label for='data-f181e2ea-64fc-40bf-ac9b-b024e3a3004e' 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",
       "       [ 9.165,  9.136,  9.08 , ...,    nan,    nan,    nan],\n",
       "       [ 9.099,  9.08 ,  9.045, ...,    nan,    nan,    nan],\n",
       "       ...,\n",
       "       [11.981, 11.981, 11.981, ...,    nan,    nan,    nan],\n",
       "       [12.992, 12.992, 12.992, ...,    nan,    nan,    nan],\n",
       "       [12.904, 12.904, 12.905, ...,    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-1194e753-b82d-4cdb-b4a9-33da8a9488eb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1194e753-b82d-4cdb-b4a9-33da8a9488eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8ba75f7-c787-426e-b0c6-6976e9620258' class='xr-var-data-in' type='checkbox'><label for='data-e8ba75f7-c787-426e-b0c6-6976e9620258' 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",
       "       [50., 51., 52., ..., nan, nan, nan],\n",
       "       [50., 51., 52., ..., nan, nan, nan],\n",
       "       ...,\n",
       "       [51., 52., 53., ..., nan, nan, nan],\n",
       "       [51., 52., 53., ..., nan, nan, nan],\n",
       "       [52., 53., 54., ..., nan, nan, nan]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4c08412b-6f69-402d-95fd-ec12bedca1fb' class='xr-section-summary-in' type='checkbox'  ><label for='section-4c08412b-6f69-402d-95fd-ec12bedca1fb' class='xr-section-summary' >Attributes: <span>(117)</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><dt><span>TIME_QC._FillValue :</span></dt><dd>-127</dd><dt><span>TIME_QC.conventions :</span></dt><dd>Copernicus Marine In Situ reference table 2</dd><dt><span>TIME_QC.valid_min :</span></dt><dd>0</dd><dt><span>TIME_QC.valid_max :</span></dt><dd>9</dd><dt><span>TIME_QC.flag_values :</span></dt><dd>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]</dd><dt><span>TIME_QC.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>TIME_QC.long_name :</span></dt><dd>Time quality flag</dd><dt><span>POSITION_QC._FillValue :</span></dt><dd>-127</dd><dt><span>POSITION_QC.conventions :</span></dt><dd>Copernicus Marine In Situ reference table 2</dd><dt><span>POSITION_QC.valid_min :</span></dt><dd>0</dd><dt><span>POSITION_QC.valid_max :</span></dt><dd>9</dd><dt><span>POSITION_QC.flag_values :</span></dt><dd>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]</dd><dt><span>POSITION_QC.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>POSITION_QC.long_name :</span></dt><dd>Position quality flag</dd><dt><span>DIRECTION._FillValue :</span></dt><dd> </dd><dt><span>DIRECTION.long_name :</span></dt><dd>Direction of the profiles</dd><dt><span>DIRECTION.conventions :</span></dt><dd>A: ascending profile, D: descending profile</dd><dt><span>DIRECTION.string_length :</span></dt><dd>[991, 992]</dd><dt><span>PRES_QC._FillValue :</span></dt><dd>-127</dd><dt><span>PRES_QC.conventions :</span></dt><dd>Copernicus Marine In Situ reference table 2</dd><dt><span>PRES_QC.valid_min :</span></dt><dd>0</dd><dt><span>PRES_QC.valid_max :</span></dt><dd>9</dd><dt><span>PRES_QC.flag_values :</span></dt><dd>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]</dd><dt><span>PRES_QC.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>PRES_QC.long_name :</span></dt><dd>Sea pressure quality flag</dd><dt><span>PSAL._FillValue :</span></dt><dd>-2147483647</dd><dt><span>PSAL.scale_factor :</span></dt><dd>0.001</dd><dt><span>PSAL.add_offset :</span></dt><dd>0.0</dd><dt><span>PSAL.standard_name :</span></dt><dd>sea_water_practical_salinity</dd><dt><span>PSAL.long_name :</span></dt><dd>Practical salinity</dd><dt><span>PSAL.units :</span></dt><dd>0.001</dd><dt><span>PSAL.valid_min :</span></dt><dd>2000</dd><dt><span>PSAL.valid_max :</span></dt><dd>41000</dd><dt><span>PSAL.ancillary_variables :</span></dt><dd>PSAL_QC PSAL_DM</dd><dt><span>PSAL.data_mode :</span></dt><dd>D</dd><dt><span>CNDC._FillValue :</span></dt><dd>-2147483647</dd><dt><span>CNDC.scale_factor :</span></dt><dd>0.001</dd><dt><span>CNDC.add_offset :</span></dt><dd>0.0</dd><dt><span>CNDC.standard_name :</span></dt><dd>sea_water_electrical_conductivity</dd><dt><span>CNDC.long_name :</span></dt><dd>Electrical conductivity</dd><dt><span>CNDC.units :</span></dt><dd>S m-1</dd><dt><span>CNDC.valid_min :</span></dt><dd>0</dd><dt><span>CNDC.valid_max :</span></dt><dd>50000</dd><dt><span>CNDC.ancillary_variables :</span></dt><dd>CNDC_QC CNDC_DM</dd><dt><span>CNDC.data_mode :</span></dt><dd>D</dd><dt><span>TEMP_QC._FillValue :</span></dt><dd>-127</dd><dt><span>TEMP_QC.conventions :</span></dt><dd>Copernicus Marine In Situ reference table 2</dd><dt><span>TEMP_QC.valid_min :</span></dt><dd>0</dd><dt><span>TEMP_QC.valid_max :</span></dt><dd>9</dd><dt><span>TEMP_QC.flag_values :</span></dt><dd>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]</dd><dt><span>TEMP_QC.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>TEMP_QC.long_name :</span></dt><dd>Sea temperature quality flag</dd><dt><span>PSAL_QC._FillValue :</span></dt><dd>-127</dd><dt><span>PSAL_QC.conventions :</span></dt><dd>Copernicus Marine In Situ reference table 2</dd><dt><span>PSAL_QC.valid_min :</span></dt><dd>0</dd><dt><span>PSAL_QC.valid_max :</span></dt><dd>9</dd><dt><span>PSAL_QC.flag_values :</span></dt><dd>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]</dd><dt><span>PSAL_QC.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>PSAL_QC.long_name :</span></dt><dd>Practical salinity quality flag</dd><dt><span>CNDC_QC._FillValue :</span></dt><dd>-127</dd><dt><span>CNDC_QC.conventions :</span></dt><dd>Copernicus Marine In Situ reference table 2</dd><dt><span>CNDC_QC.valid_min :</span></dt><dd>0</dd><dt><span>CNDC_QC.valid_max :</span></dt><dd>9</dd><dt><span>CNDC_QC.flag_values :</span></dt><dd>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]</dd><dt><span>CNDC_QC.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>CNDC_QC.long_name :</span></dt><dd>Electrical conductivity quality flag</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (DEPTH: 2858, 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: (12/117)\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",
       "    CNDC_QC.conventions:            Copernicus Marine In Situ reference table 2\n",
       "    CNDC_QC.valid_min:              0\n",
       "    CNDC_QC.valid_max:              9\n",
       "    CNDC_QC.flag_values:            [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
       "    CNDC_QC.flag_meanings:          no_qc_performed good_data probably_good_d...\n",
       "    CNDC_QC.long_name:              Electrical conductivity quality flag"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate a filtered xarray with all variables for selected Platform, for a certain DEPTH range\n",
    "for var in vars_sel: \n",
    "    \n",
    "    if metadata[pc]['depth_m_v1']==0: align_and_nan = True\n",
    "    else: align_and_nan = False\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=[metadata[pc]['depth_newindex4xr_v1'],\n",
    "                                         metadata[pc]['depth_newindex4xr_v2']])\n",
    "\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": 47,
   "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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e6HdOMiyEP/hPoaOHtm7muH3wB+Di6Xxn89qQeBTYXx7PZyPNHkBuTu+3WrkR2EDS/JLmxx+yV6Xz2Bs4VykWTmJfXJkOwV3IXAklXeYXetRL4kpzGP6i81fliFUkd6P/bVzpF6gWFqBSW3/Fld+SeIiDWWEOcp53MT9KQ1pPqvMQ8FrAc5ae8InnKPr9VWBz4APriMApOvtcE7C03GVPgd+nIbCH1Dlc9pdwrw8npPzni2RtPD2h1fA3+8m4O5IJwD9T+mw9mOzbSVEdL+EroQv7S+Fvlq109GBW7IJs/8Afti14l3UaMG8mf2/g3jJldwGeLjrPg+bwWvUH/gNckEm7CHfvnz3uIeDA9LmN1JtJ+wVPuipR9/bAz2qQ5z78oT0Bf5ufWPQ9HJ/SJ6Tj2oCjMvkzKfE2h79BGjAsk/afVMeCXbx2w/E31YH4W+T2ZHow+AO6DVggU+b3uNv2wrk8UFTn8czeQ7sPODaz/yPcU3Q5ufZN5zYF740cndLn6H6rci22pHQPZtZ1x3sdDxblXwAcl7me12TyBqXrt0zan/X7zbT5GZmeO64kNqlyb01MdT0DLFvhnP4JHFGtLTzA3wxgtUzerB5MtfMu0e4GeI+oFQ91MAn4csr7TfYapbQrgeNzfEdL472fvYvkfAhYDFeOj6Vrs1TK/xI+5DkQV5qTgJVS3q/SscfjLz1b4M/dNbrye+rK1pM9mF2sI3bHLl0ovxxwU+at9SX8Zl8ic0xXXLy/AIwyd5+/HP4Qfj/TzgX4Ww6SFpd0jTyW+Kf4UMqQovq67GZe7mH5cvyBfVgmazL+FpdlMB1xRorzBwOTC0/VAmY2w8xux3t4O5Gfw8295s6Dv1FeL2mdTP516XudDx/W2V8dxhJj8ZeBYpbCh0HHA0jaD39R+A/uHLPLmPs8Oylt2bfBocA4c39aBfK4cC9FOff6peS50sy2xntFhwInymPQ1ON+q5Vh+ENoQmr/S0W9wX3p7Pgy69Z/Mj6kNLRC/Z9Y5+BqFa8Nfm8tiM8dLow/dAEPCyDp0dRzmIA/3LPnX66txeiY15ztPMh33rMws6fM7BPzuDH/xhXIt1N2xd+mOhsGzBpNSfONI4BzzWPVFDgZdyv0DD70/U9cWX6UZHnMzCaZx6O6FFdGO6Syn6Vjf2cea+p+3KFtI5yRlqTph8gSViLtXWB76xxkah7zAFyVytXCu/gb5ZBMG4OtI557NVf9s8kgD2tczsX87ZnjhPdUlsDnXrL+vV6gIwBVoYu/Eh1DaJ3y0+dKsclbU/maMA8O9iA+jFHypjUPDnU7HYGd/oM7VizmO8AjZjZVHmPnT3hs9h8A35FUGIIpWGGVddVfRtxL8OGTXTNpY4BFJGXjs1Rz4T6n91RHRa7g/4EPoaxNfe63WtkVeMp8mPVd4P6i39QgM8t6Ls669R+EzxnU3UDHzJ7H397/moZa5yQswMd4zzkbiiIbjiDPeVcUNyPHC8A66fdbYJ2UjnU2DHgHIA3jjQBuMbNOQ7nmTlYPM7NhZrYi3uN90pJRQRVZnsspf8OYWxTMh8CiReOO5+Ohc5cDfwNQDea2eTAf9x8BnClpsDwc7kqStkiHVHPVX6rO7a28i/msKfJ5+Dj7jja7B9+bgLUl7SZpHuC3+LhvwQPzZcCRkobJY1T8HB/eQNLq6U1wXnl4gP3wcd/7U/7ycnPT5fNcI0mb4hP9JRWY3KX8dpn8E4DN5PHPF5GbIP8Edyn/y3TMOfiw6b3pOzgK+Ft6yGBmp1S4hiXfjtOb7fGZNjCPCPkwPoY9T+qFHUyaVyjDh8Dy6hy/JzeSDpT0zXTeLXLz87XwiKRzdL8pX7iEwtzYMEnH4fM7hZhA/8Jd2n833Rv9JX1RHeGaAXaQh6segPcIH0vXsXBtKrnMr5VL8d7bTsxBWID0ML4ROF5utrs6HSGwId95z0LS7nJz+hZJ2+CKvuBB+j58JOVwuYlxYeThnjJ1DQbuBB4ys6NL5A+TNDR9Z5vgQ3DHpbyFJG2b7t1WSfviv+U7U/EH8KHhY1L+l/GhxDuL22kY3TEOV7xRgxVZJu9iXHtPoMOK7Ejc4mMSPsl9Sjp2+VRXdjx2WfzHWXZMNx13PJkxdvyt9zx8Ingi3l3dK+Wthbsvn4x3YX9OZpy73HnmuD7LJfk/p8NybjKwb+aYrXEz08/wm3r5TJ5w095xaTudDsema+DjuJPStXwc2DVT9qtJ7v5lZLuvSK7XyMzhpOs3I5P/Pv4yMF/mmLXxH/Wn6Zj7gK+kvF3wN+KFitq9Gw8VXMt1LL6HWvBAZpZJWzrJMi7dQ4eWuxdS2qJ4CO3x+Jt/4ZpkrZ8OJGOhVFT+2/gwxvh0/s+T5s7qcL/dTcYKrqjdLemwxJySrvH1FM2H4JZHt+EP8k/wB+N6metZsCKbjD/AVsiUPTR93xPwHumWzD53+hZlfhPF1zGl/ZJk5Qb8GFdiE/Ch42uoPE87qy18mOw2OqzITgPuznPeJeR8MH03n+IGNHsV5a+fvqfPgKeA9SvcowfQ2fqusC2b8jdP5zEVf9ZlnwGLpXMp/JYfBb5RVP9aeBiMKbihza7lZGnEFt6Ug05IOhb42Mwu6GlZgvyoG8IlpN7RaDMrXocx1yHpNGBJMzugp2XpzczxwrGgd2Fm4Z5nLsQ8cF3JIZ3Ah4bxYbbngS/iQ6Hh3qfBhIIJgqAvsAC+5mkoboF1Jp1XzAcNIIbIgiAIgoYwt1iRBUEQBHMZoWCCIAiChhAKJqgrmgOX7WmNyH/rUVdvR9Kukt5Ni0vX72l5gqAUoWCC2UgL6R6Wx48YJ3ei1+W4IZJekbRqZv94FcVpqReaPR7NrCB2mWOOkMdjmSKPV7NqiXouaaSCq0PdZ9Dhpv3pEvWXjfmTFnr+V+4W5QNJf1PGm0FaIHhxKvuBpCOL6t5R0sh0bR+WtOYcnEfQiwkFE3QirSz+F/AX3A3IMHz1/bQu1rcS0GJmr6R94fFQxpHxaFtnZsWjsaIgdvLIkwfjHq4H4b7UxhbJ/BW64Dqnm1mOyu5/jqB8zJ8FcTcsBa/MSwN/yJQ9HneQuhzwNeAoSdsBSFoF93RwKO5L7VbgFtUhVk7Q+wgFExSzKoCZXW1mbea+kEaY2Sy/RpIOSm/+4+UR+JarUN83cZ9RBb6KP9iOAPZS+UBMdUfu3uU43PPAi+a8bmbjMse04sr1sHL1ZI5dQx5RcIKkF5RxGKqiMMrZ4T91RBV8NvUC9iwlq6Rj5VFNP5J0maQFU+9iMu4h+FlJr5eSzcyesw7Hj4Y70Vwm5V1lZneY2VQzGw/8Dfhypvj+wElmNt7MXkr5B6a8bXHPw/9N9Z+Gv4RsQRAUEQomKOYVoE3SpXKfZZ1id0jaBfdd9W3cVcWD+PqCcuyAu+AocAD+1luIF/KtPEKpthgwO6ahvRckZR0WLp22tdP8xZuSTlBnv2I/w130V3QUKA9beyvuO2xx4CfAlcqEuy2HlY+dkuXAtH0N9+81CDjH3GvuoEz5sj0t5Y/5szmpN5S+76GUjydUKj6JcPc/QdCZ7vRLE9vcseHDJsNxf1gzcUd+S6S824GDM8e24H6Slkv7WX9y8+F+nebJ7H+Kh2oAd0V/c6auA+kcZXBWXTXIvib+gOwHbIb7xto75W2W6rwNH95ZHleo30/5y+C+1Ras1j7eE/sAH/4rpF1NivtBFf9k1c4N9yv2o8z+aqR4R7VcG6rE/MEDwI0HVs1cAyt8Z5lj3kqfV8f9Wm2Jr4z/De7j7Jievm9ja74tejDBbJjZS2Z2oJktjb+ZDgXOStnLAWdneg7j8DfYYSWq2goP7fx52t8VV1iFIbMrge3lsTDqJfuLZjbGfHjvYeBsPFIluPNB8HjqE8xDCVxAR/yMs4ATzaObVmMoHrq2PZNWHEtmThia6svW3UrneEdVsQoxf+Teea8Cdrc0R4Y7WoTZ4wlNSvWNwnuh5+DKewjuRHF0LXIFfYNQMEFF0gNlOB1DIO/isdAXymzzpod5MaWGxwYB70j6AI8e2h+P2lgRdS0GDHSOj/EyHrytnPuKrYA/JMupQgCxRyTtU+LYMcAyRcNr2VgyU/AeW4GSwasqMAZX5tm6Z+LehLtCp5g/ctPmW/CIq3cX0s3nZN6nQjwhM7vezNY2s0XxOa3lcK++QdCZnu5CxdZcGz4E8nNg6bS/DO5e/m9pf1fc5f1aaX9BYI9M+ewQ2Vt0uB0fhsfJ2AZ/2Ba2U/EASlCfIbKd8UiIAjbGH/gHZPIvw63kFsDnY0aRhvzwuZSsbIaH3J23RDsDcPf+R+NKckv8LX/1lH8yPkw2Hx6m+dWic/sA2KbCefy/VGYFXClfT+cwEpWG71bHh8XmTbLthyvWDVL+2rii2rNM+VPx+EALp7reJxPmGtgQH4JcDJ9Lu6qn79vYmnPrcQFia64tKYLr0oN5Svp/ATA4c8x3ca+0n+I9moszeZYeqGsDIzPpRxcUSVF7Q/G5hbXrpGCuxud9JiflcXhR/mA8jsikJPtvST75StRVbZ5krfQgnkhRrA186GhEauch3PQ3e26dYqeUqLslyfYuHqPkCmDhPLJRPebPJXTEhilsL2TyB+Lxlz5NiujIovr/m+oel+6N+TN5+2briq1vb+HsMmgIko7CQ/8e1dOyBEHQM8TiqKBRvIWb8QZB0EeJHkwQBEHQEMKKLAiCIGgIMURWhUUWabF+Sy7MzPZ+tJuY2d7CGgvWa6lDEAS9iSeffHKsmXV5Xde2X5vfPhnXlq+t56bdaWbbdbWt7iAUTBWWW6aV/9w+L4sOjXVkQRBURtLb1Y8qz9hxbTx259K5ju2/1OtDqshyMe6K6SMzWzulnYSb8rfjoaMPNLMxcyJzJWKIrAqftM0TyiUIgm7CaLP2XFsOhgPFPZw/mNk6ZrYevh7st/WVvzOhYKowf8s0zh61dU+LEQRBH8CAdizXVrUuswfwtUrZtE8zu/NT3qtFXQgFU4W3P1ica07cnk33ObOnRQmCoA/QnvOvq0g6WdK7+KLY6MH0JP2mG61T2+k/petfaBAEQR4MY4a159qAIZKeyGyH5GrD7NdmtgzubLZq3KM5ISb5q6A2Y+D4Gfzn/l/1tChBEPRyDGjLP2o11sw2moPmrsKd0R43B3VUJHowVWhvbeHzId0WdDEIgj5OveZgSpFCXhfYCffX1zCiB1OF1Vdagv/e8H89LUYQBH0AA9rq5F1F0tW4l+8hkkbjPZUdUtTVdjzG0KF1aawMoWCCIAiaiHrN9ppZqThLF9Wp+lyEgqnCy69+UP2gIAiCOmBYLXMwTU8omCAIgibBDGb0Hv0SCiYIgqB5EG2zInzP/YSCCYIgaBIMaI8eTBAEQdAIogfTh1htlSV7WoQgCPoIvtAyFEwQBEFQZwyYYb1n/XsomCAIgibBEG29yMFKKJggCIImot1iiKzPMPKDD1ntxD/ROhUGfApqwz24GTx1/s96WrwgCHoRMQfTx7BWY/oi7bQPaMH6wQLvGo9ddmRPixUEQa9EtMUcTN9hwETx5mE/72kxgiDoA3hEy1AwfYYZ88NqJ/2Jfp9D61ToNw36TzHmmdBG/0kzufueY3paxCAIeglmYrr162kx6kYomCq0zjeTactNo/XDAcycVwycAC0zxQM3/6KnRQuCoBfSHnMwfYe2Sa0s9L+BzPtJOy0z2nno+ogNEwRBY/BJ/hgi6zO0z9vOxE0+Z0J7C7TDchedjma0oBmiZZroN11+VxjIoGUGvHRiWJcFQdAVYpK/T6FpLQx6ah5ap8G8H7fz8HUx4R8EQWOISf4+RstMGDjRGPTeDPp93sY3NvsdSiFNRzzymx6WLgiC3kZbLLTsO2jwTMZuMYPxY/vTf9IABo73xZYtM3pasiAIehuGmGG957Hce86kQcyc1srAtwfQMgM0EwZOMP43PBZaBkFQf2KSv4/Rb2AbbatNYUabf+lT12hh+b/9gXnG+KUbdVxM6AdBUB8MxRBZEARB0Bh60yR/05yJpIslfSRpZCZtXUmPSHpe0q2SBmfy1kl5L6T8eSTNJ+k2SaNS+qmZ4wdKulbSa5Iek7R8N59iEARBRcygzVpybXMDzSTlcGC7orS/A0eb2ReAm4BfAEhqBa4ADjWztYAtgcK0+xlmtjqwPvBlSdun9IOB8Wa2MvAn4LTGnUoQBEHt+CR/v1xbNcq8tP8hvYA/J+kmSQs18nyaRsGY2QPAuKLk1YAH0ue7gN3S522A58zs2VT2EzNrM7OpZnZvSpsOPAUsncrsDFyaPl8PbCWp9wx2BkHQK2ijJdeWg+HM/tJ+F7C2ma0DvAI01Jli0yiYMowEdkqf9wCWSZ9XBUzSnZKeknRUccGkmXcE7k5Jw4B3AcxsJjARWLRUo5IOkfSEpCdmTpxar3MJgiCoiCHaLd9Wta4SL+1mNiI9/wAepeMFvCE0u4I5CPixpCeBBYDpKb0V+Aqwb/q/q6StCoXSENrVwJ/N7I1Ccon6rVSjZnahmW1kZhu1Ljhffc4kCIIgBzX0YIYUXoTTdkiNTR0E3F7/M+igqa3IzGwUPhyGpFWBb6as0cD9ZjY25f0b2ICO3sqFwKtmdlamutF4D2h0UkALMvuQXBAEQY9hQHv+CfyxZrZRV9qR9GtgJnBlV8rnpakVjKTFzewjSS3AscD5KetO4ChJ8+G9mi3wiXsk/Q5XHv+vqLpbgAOAR4DdgXvMrGQPJgiCoGdQw0MmSzoA+BawVaVnoKQ8K8qnmNkF5TKbZohM0tX4w381SaMlHQzsLekVYBQwBrgEwMzGA38EHgeeAZ4ys9skLQ38GlgTeErSM5IKiuYiYFFJrwFHAkd339kFQRBUx6BuVmSlkLQd8EtgJzOrNsH8C2AQPj1Rbqvo/bdpejBmtneZrLPLHH8FbqqcTRtN6bkWzOxz3FAgCIKgKTFTLUNkFUkv7VviczWjgeNwq7GBwF3JiPZRMzu0TBWXm9mJVdqYv1J+0yiYIAiCgLotoizz0n5RDeVns86t9ZhQMEEQBE2Cx4NpjuV5aQ5mopldVJT+E6BfkRFVSULBBEEQNA1NFdHyINw6t5gL8fnvs6pVEAomCIKgSXAz5ebowQCWPKIUJ07L6wUlFEwQBEGTUPBF1ixIWsLMPixOy1u+afpiQRAEgbvrz7N1A38AbpO0haQF0rYlcCtwRp4KogcTBEHQJLi7/uYYIjOzyyR9DJwIrI2P4L0AHGdmuVzMhIIJgiBoIppoDoakSCoqE0nHmNnvS+XFEFkQBEGT4N6UW3JtTUTZBezRgwmCIGgS3FVMUymPPJTtcoWCCYIgaBrq5yqmGynrMDMUTBAEQRPRLCv5ayB6MEEQBM1OM1mR1cA/ymXUvS8m6dMq26Tkgj8IgiAootkm+SWdLmmwpP6S7pY0VtJ+hXwzO6Vc2UZI+bqZDa6wLQBMaUC7QRAEczVuRZZv60a2MbNP8SBlo4FV8VgxVWnEENludTomCIKgT2HAzOab5O+f/u8AXG1m43K6ImtID2aVchmS9gAwszca0G4QBMFcT7MNkQG3ShoFbATcLWkx4PM8BRsh5b8l3StpWIm8YxrQXhAEQe8g5/BYdw6RmdnRwKbARmY2A5gK7JynbCMUzHPAVcCjhR5LhrnOPCIIgqC7KAQcy7N1F5LmA34MnJeShuK9mao0QsGYmf0N2Ao4StIlSUCosCAnCIIgoOl6MMAlwHRgs7Q/GvhdnoING8gzs1fwbtWHwNOSvtSotoIgCHoDhYBjTaZgVjKz04EZAGb2GTlHoxphRTarYTObCRwt6Q7gamCxBrQXBEHQKzDEzPamsyKbLmle0giUpJWAaXkKNkLBnFCcYGb3SdoQ+EED2guCIOg1NKGrmOOAO4BlJF0JfBk4ME/BuisYM/snzAqrOQzXemNS2M1T691eEARBr8GaKx4MgJndJekpYBN8hOoIMxubp2wjXMWsJ+lR4D7gdDzs5v2SHpW0fr3bC4Ig6C3Ucw5G0sWSPpI0MpO2h6QXJLVLymUJJl9VuT2woZn9C5hP0sZ5yjZisG84ruHWMLOt07Y68NOUFwRBEJShjpP8w4HtitJGAt8GHqhBpHNxg6290/4k4K95CjZiDmZ+M3usONHMHpU0fwPaC4Ig6BUYoq1Ok/xm9oCk5YvSXgLI6+ol8SUz20DS06mO8ZIG5CnYCAVzu6TbgMuAd1PaMsD++ERREARBUIYaJvmHSHois3+hmV3YAJFmSOpHhxXZYkB7noJVFUyarD8FGGpm20taE9jUzC4qdbyZHS5pe9yVwDB8Umg08Fcz+3ceoYIgCPoiVtsk/1gzyzWPMof8GbgJWFzSycDuwLF5CubpwQzHV3L+Ou2/AlwLlFQwAGZ2O3B7HgGCIAiCDqyJrMgktQBvAkfh3lkE7FIYaqtGHgUzxMyuk3QM+OJJSW0VBGoFDgZ2IWOmDNwMXJScpQVBEASz0e2r9CtiZu2SzjSzTYFRtZbPo2CmSFqUjvG3TYCJFY6/HJiAL7gcndKWBg4ArgD2rFXIIAiCvkK9ejCSrga2xOdqRuMLJscBf8G9qtwm6Rkz27ZKVSMk7QbcaGY1+ZPMo2COBG4BVpL0UBJs9wrHb2BmqxWljca9K0eo5CAIgjKYQVt7fRSMme1dJuumGqs6EpgfmCnpc3yYzMxscLWCVRWMmT0laQtgtVTxy1WGucYnN/03mFk7zBrH2wMYX/VUgiAI+jDN5iomhbnvEnmsyH4MXGlmL6T9hSXtbWbnlimyF3AacK6kgkJZCLg35QVBEAQlMJprkh9A0gYlkicCbyeHxmXJM0T2fTObtWozLbL5Pr66czbM7C3SPEuau1FevzVBEAR9m+aa5E+cC2wAPJ/2vwA8Cywq6VAzG1GuYJ4loy3KLPtMC25yreIE+meVi6Qlc5YLgiDok5jl27qRt4D1zWxDM9sQWA93ObM17m+yLHkUzJ3AdZK2kvR1PK5L3hX5xWtlyq6dKeOYbV1Jj0h6XtKtkgZn8tZJeS+k/HlS+oZp/zVJfy4oR0kDJV2b0h8rdqEQBEHQDJgp19aNrF6YInH57EVc4bxRrWAeBfNL4B7gh3hc5rvxRTdVMbNvVtovYjizO2b7O3C0mX0Bt3z4Bcxaa3MFcKiZrYWb4hUMD84DDgFWSVuhzoOB8Wa2MvAnfJ4oCIKgaXArspZcWzfysqTzJG2RtnOBVyQNpOO5W5KqUppZu5mdZ2a7m9luZnaBmZVdaFlA0hKSNpC0fnI3U62dB3Ab7Syr0eH18y5gt/R5G+A5M3s2lf3EzNokLQUMNrNHkr32ZfiCT3DXNZemz9cDW2WH/oIgCJqBJhwiOxB4DfeI/zPgjZQ2A/hapYJ5rMi+DBwPLJeOL9hAr1jm+PWA84EFgfdS8tKSJgA/MrOnqrWZYSSwE+4FYA/caSbAqoBJuhNfl3NNihk9jI7FnaTPw9LnYSTnm8kbwURgUWA2AwRJh+C9IPovtmAN4gZBEMwZzWZFZmafpV7Lv8zs5aLsyZXK5rEiuwjXWk8CVXsu+FDXD4pd9icPAJcA6+aoo8BBwJ8l/RZf7Dk9pbcCXwG+CEwF7pb0JPBpiToKur7Ut1byPSB5JL0QYL5Vhnbvu0IQBH0Wo9vnV6oiaSc8cOQAYIXUiTjRzHaqVjaPgpmYnFfmpW7xYMxsFD4chqRVgcIczmjg/oKFmqR/42Z0V+BuaQosjftBK5RZBhid5nAWZPYhuSAIgh6lCd9ojwM2xqMUY2bP5DWSyjNTdK+kP0jaNM2pbFBm4U2B2yXdJmlPSZulbc8UI6ameDCSFk//W3D30OenrDuBdSTNl5TFFsCLZvY+MEnSJml+ZX98eA28B3RA+rw7cE+tfnWCIAgaioG1K9fWjcw0s0r+J8uSpwfzpfQ/G3fAgK+XOrir8WDKOGYblDwJANyID7EVFnv+EXg8yfJvM7stHfdDfJhuXjxkQKH3dRFwuaTX8J5LeBUIgqDpaLYhMmCkpH2AfpJWAQ4HHs5TMI8vsopWAmXK1BwPpoJjtrPLHH8FPiRWnP4EsHaJ9M9xQ4EgCIKmpQnHVX6CxwObhq+DvBM4KU/BsgpG0n5mdoWkI0vlm9kfy5SLeDBBEARdoBl9kZnZVFzB/LrascVU6sEUJuRr9aQZ8WCCIAi6ggFNomAk3UoFm4M5siIzswvS/xNqlCviwQRBEHSRJhoiOyP9/zawJB1TEnvj/smqUtWKTNLpkgZL6i/pbkljJe1Xoch4SXsky69CHS2S9iTiwQRBEFQgnwVZd1iRmdn9ZnY/7ndsTzO7NW374OsQq5LHTHkbM/sU+BbeE1mV5BOsDHvhZsAfSnol9Vo+wLVgWG4FQRBUwnJu3cdikmZ5bpG0Au5BpSp5zJT7p/87AFeb2bhKLrwiHkwQBEEXseab5Mc9udwnqeA9eXmSK61q5FEwt0oaBXwG/EjSYsDnOQXrb2YfFHYkLZndD4IgCIponjkYAMzsjrT+ZfWUNMrMpuUpm8eb8tHApsBGycR4Kr6IMg+548EEQRAE4GvT82wNliLjscXMppnZs2mbVuqYUuTpwWBm4zOfpwBTcparJR5MEARB0N7TAsziEklbUlmbXQSsXy4zl4LpCikGzKyFlmb2YaPaCoIg6BXUcR2MpItx46yPzGztlLYIcC0+j/IW8J1sB6KIBXEv+pUE+riSDHVXMHWOBxMEQdCnqOM6mOHAOXjgxQJHA3eb2amSjk77vywthy0/pwLkDTj2jJlNSetfNgDONrO3yxQZTv3iwQRBEPQt6qRgzOyBEm71d8adCoNH+L2PMgqmHuRZB3MeMFXSusBRwNt01ojFlI0HQ4f7mSAIgqAUpnxb11gihTUh/V+8bnKXIM8Q2UwzM0k74z2XiyQdUOH421Psl8tIIYrxQF/7U2M8mCAIgr6G8vdghkh6IrN/YYrG2zTkUTCTJB0D7AdsLqkfHYsvZ6Or8WCCIAj6PCbI7wZmrJltVP2wTnwoaSkze1/SUsBH1Qqk4I37Aiua2YmSlgWWNLP/VSubR8HsCewDHGxmH6TK/1CpQFfiwQRBEAQ0eqFlIbLvqen/zZUPB+Bc3Hj668CJwCTgBuCL1QrmUTC7A5cUTNnM7B0qzMFEPJggCII5oE4KpkyU4FOB6yQdDLxDviCMXzKzDSQ9DbMiCg/II0MeBbMk8Likp4CLgTurxLKPeDBBEARdpX5WZOWiBG9VY1Uz0tSIASR3YbmWg+YJmXyspN8A2wDfA86RdB3eG3m9RJGIBxMEQdAVmijgWIY/AzcBi0s6GR/VOjZPwbyuYkzSB7jb/ZnAwsD1ku4ys6OKDh8vaQ/gBjNrB48Hg3fF5rp4MP3HwHLntjDgjY8Y9YtlaDGgTcwYZPSbJtY89k+YoPUzsBZomeFWIPOONQa9+zntrS3cc/fRPX0aQRDMJdRgRdZw0rP7TXyJyla40dYuZvZSnvJ5Floejg9vjQX+DvzCzGakhl9NDWfZCzgNOFdSQaEsBNxLxIMJgiCoTBMpGDNrl3SmmW0KjKq1fJ4ezBDg28Ur91PD3ypx/JJmFvFggiAIukAz9WASIyTtBtxYZf59NvLMwfwWQNLiwDyZ9HfKdJPOxd3JYGaf1CJMEARBn6f55mCOxL2wzJT0OT5MZmY2uFrBPENkOwJ/BIbii3KWA14C1poTiYMgCIIiuj8cclXMbIGuls0zRPY7YBPgP2a2vqSvAeXM3wBWlHRLuUwz26lGGYMgCPoOTaZgJG1eKt3MHqhWNo+CmWFmn0hqkdRiZvdKOq3C8R8DZ+aoNwiCIChCzRNwrMAvMp/nATbG48R8vVrBPApmgqRBwAPAlZI+wk2VyzHJzO7PUW8QBEFQTJP1YMxsx+y+pGWA0/OUzeOuf2fgM+BnuDfk14EdKxz/Vp6GgyAIgs7I8m89yGhg7TwH5rEim5LZvTTH8d8ulyfpG2Z2Vx7BgiAI+iRNZkUm6S909KtagPWAZ/OULatgJE2iQmctj4laCS4Clu1CuSAIgr5Bkw2RAdmYMzOBq83soTwFyyqYgmmapBNxFzGX4/bP+wJlzdYqWJAJWDSPUEEQBH2VJlxouZCZnZ1NkHREcVop8kzyb2tmX8rsnyfpMcpP8nwVD042uShduPVBEARBUAprSiuyA4BiZXJgibTZyKNg2iTtC1yDd972BtoqHP8oMLWUJZmkl3O0FwRB0Hdpkh6MpL3xYJMrFI1MLQDk8tKSR8Hsg2uqs/FTfyillcTMtq+QV3LBThAEQZBoEgUDPAy8j/ujzK5tnAQ8l6eCPFZkb+GmyjUjaRGvwuY6N/1BEAQ9QbPMwSQHx28Dm3a1jrLrYCQdUq1wqWMkLSvpGkkfA4/h0TA/SmnLd1XQIAiCoPuRtImkxyVNljRdUpukT/OUrdSDOVpSJTf7Ao4ALixKvxY4C9jXzNqSgP3wgGPX4H7NgiAIglI0SQ8mwzl4LK9/ABsB+wMr5ylYScHcT+UV+wClFk0OMbNrswlJ0Vwj6aRyFUm6GPgW8JGZrZ3S1gXOBwbhHgL2NbNPU0/oJaBgNPComR2ayuwN/Ar/msYA+5nZWEkDgcuADfEJqj3T8F8QBEFz0JxWZJjZa5L6pWf5JZIezlOu0jqY73VRliclnYuv+n83pS2Dm7o9XaHccFxTXpZJ+zvwf2Z2v6SDcKdrv0l5r5vZetkKJLXixghrJqVyOnAYcDxwMDDezFaWVIi6uWcXzzEIgqAxNF8PZqqkAcAz6Zn6Ph4fpip5fJHVyv7A88AJwJ3AiPR5JPDdcoWS6+dxRcmr4U42wXtLu1VpW2mbX5KAwXgvBtxQoeDq5npgq3RMEARBUyCa0hfZd3FdcRgwBe8wVHsWA/nMlGvCzKYD56VtThkJ7ATcjM/hLJPJW0HS08CnwLFm9qCZzZD0Q1zBTQFeBX6cjh9G6lGZ2UxJE3HPArPNMyXjhUMA5hmwYB1OIwiCICdN1INJ8+cnm9l+wOd4ZyE3jejBzIakp7pY9CDgx5KexBf3TE/p7wPLmtn6eDjPqyQNltQf+CGwPh6B8zngmIIYJeov+VWa2YVmtpGZbTSgdb4uih4EQVAjdfamLOkISSMlvSDppzWL43Mui6UhsprJEzJ5CeAUYKiZbS9pTWBTM7uohna6NBRlZqOAbZIcqwLfTOnTgGnp85OSXgdWLbRjZq+nMtcBR6fqRuM9oNFprmZBZh+SC4Ig6FnqNMkvaW3g+7iLrunAHZJuM7NXa6zqLeChtJp/lnd9M/tjtYJ5ejDD8bmUoWn/FeCnNQp4W43HAyBp8fS/BTgWtyhD0mKp64akFYFVgDeA94A1JS2WqvgGbm0GcAtuaACwO3CPmTVRZzQIgqCuPZg1cAvbqWY2E7cM3rULIo0B/oXriwUyW1XyzMEMMbPrJB0Ds+YvKvkimw0zO7baMZKuBrYEhkgaDRwHDJJUmEO5Ebgkfd4cOFHSTNwv2qFmNi7VcwLwgKQZ+CrUA1OZi4DLJb2G91z2quUcgiAIuoX8r71DJGVd6V9oZtl1iSOBkyUtigeN3IHOrvfziWN2AoCk+Yvig1Ulj4KZkgS01MgmwMRahUxlnzezL5TKM7O9yxSbzWOnmd0A3FCmnvNJPZ2i9M9xQ4EgCILmxKhFwYw1s43KVmX2kqTTcAvcyXiQsErh7ksiaVP8BX0QsGxan/gDM/tRtbJ5FMyR+PDSSpIeAhbDh5jKCVMuoqWAJXO0FwRB0Geppwlymiu/CEDSKfhcdK2cBWyL6wHM7FlJuRwX53F2+ZSkLfA1KQJeNrMZFYpcC1xJaT08Tx6hgiAI+ix1VDCSFjezjyQtC3ybLjquNLN3i5YN5pomyWNF1g8fu1s+Hb+NpEoWBM8BZ5jZyBJ1bZ1HqCAIgr5KnV3F3JCmOGYAP+6iZ/t3JW0GWDJXPpwO46mK5BkiuxVfYPM8+QzofoovfixFVywYgiAI+ga1zcFUr87sq3Wo5lB8LnwYbql7Jx0L2CuSR8EsbWbr5JXEzB6skFezBUMQBEFfoeDrqpkws7HAvl0pm2cdzO2StqmlUknbSjpP0i2Sbk6ft+uKgEEQBH0Ky7l1E5JWlHSrpI9TbK+b0/rDquTpwTwK3JQWO87AFayZ2eAywpyFr6q/jA6LhaWBwyVtb2ZH5BEsCIKgL9IsES0zXAX8lY4pjr2Aq4EvVSuYR8GciVsePJ9z5fsOZrZqcaKka3EvAKFggiAIytF8CkZmdnlm/wpJh+UpmGeI7FVgZA1uVT6XtHGJ9C/ixgJBEARBKVLAsTxbN3KvpKMlLS9pOUlHAbdJWkTSIpUK5unBvA/cJ+l2koNJqOjo7EDgPEkL0DFEtgxuWXZgjvaCIAj6Ls3XgykEZvxBUfpBuLRl52PyKJg30zYgbRUxs6eAL0laEjdrEzDazD7I0VYQBEGfptnmYMxsha6WzbOSv6YAM5lyHwAfAEg6Hg9bHARBEFSiyRRMWmz/TToW2wP53PWXVTCSzjGzwyTdSolTNrOdapBxJ0LBBEEQVKXZejDUvth+FpV6MPvjMZjP6Lpcs2i2tUNBEATNh1G3gGN1pKbF9lkqKZjXAczs/i6J1JkN61BHEARBr0Y0ZQ/mdknbmNmIWgtWUjCLSTqyXGal8TdJ2wK74JP8BoyRdLOZ3VGrgEEQBH2K5lMwNS22z1JJwfTDA8zUNLwVK/mDIAi6jpovknuti+1nUUnBvG9mJ3ZBmFjJHwRB0BW62c9YTmpdbD+LSgqmqxPzn0va2Mz+V5QeK/mDIAiq0IRzMLUutp9FJQWzVReFOZBYyR8EQdAlutkNTB5qWmyfpayCMbNxXZEkVvIHQRDMAU3Wgykstpc0v5lNqaVsHmeXXcLMPjCzJ4FRwFBJCzWqrSAIgl6B+RBZnq27kLSppBdJYZIlrSvp3Dxl665gsg1L+grwIm6F8LykHerdXhAEQa+iyQKOAWcB2wKfAJjZs8DmeQrmcXZZK5tkPp8E7GJmT6UIaNcB/25Am0EQBHM9TbrQEjN7V+pk99WWp1wjFEyWwWlOBjN7IzlNC4IgCMqg9qbTMO9K2gwwSQOAw0nDZdVohIJZXdJzuDJeXtLCZjY+rQLt34D2giAIegfNuQ7mUOBs3GhrNDAC+FGego1QMGsU7ResDhYBftuA9oIgCHoNTWimvJqZ7ZtNkPRl4KFqBes+yW9mbxdt01P6WDO7sd7tBUEQ9CrqOMkv6WeSXpA0UtLVkubpgkR/yZk2G3XvwUgaBBwF7Ib7IJuOe2Y+38yG17u9IAiC3kS9JvklDcPnS9Y0s88kXQfsBQzPWX5TYDNmd3w8GPdVWZVGDJFdCdyEm7V9B5gfuAY4VtKqZvarBrQZBEEw92NAfZ1dtgLzSpoBzAeMqaHsANzhcSuwQCb9U2D3vI3Xm+UzPZU/SnrczE6S9D18TUwomCAIgjLUMAczRNITmf0LzezCwo6ZvSfpDOAd4DNgRC0xXVIssPslDTezt3NLlaERCmaKpK+Y2X8l7QiMAzCzdhUZUgdBEAQd1LgOZqyZbVS2LmlhYGdgBWAC8A9J+5nZFbXI1FXlAo1RMIcCf5e0KjASOAhA0mLAXxvQXhAEQe/ArJ5DZFsDb5rZxwCSbsTnVGpSMHNC3RWMmT0HbFwi/WNJk+rdXhAEQW+ijiv53wE2kTQfPkS2FfBE5SL1pdEr+Ys5Abikm9sMgiCYe6iTgjGzxyRdDzwFzASeBi6sXGp2kmnzwcBawCwzZzM7qFrZRpgpP1cuC1ii3u0FQRD0Jurpi8zMjgOOm8NqLse94m8LnAjsSw+6ilkiCTK+KF3Aww1oLwiCoHdgQFvT+YpZ2cz2kLSzmV0q6SrgzjwFGxEP5l/AoBIr+t8C7itXSNLFkj6SNDKTtq6kRyQ9L+lWSYNT+vKSPpP0TNrOz5QZIOlCSa9IGiVpt5Q+UNK1kl6T9Jik5Rtw7kEQBHNEs8WDAWak/xMkrQ0sCCyfp2AjXMUcbGb/LZO3T4Wiw4HtitL+DhxtZl/AF2/+IpP3upmtl7ZDM+m/Bj4ys1WBNYH7U/rBwHgzWxn4E3Ba3nMKgiDoNgqWZNW27uPCZPL8G+AWfD3j6XkKdvckf1nM7IESvYrVgAfS57vwbtlvqlR1ELB6qrMdGJvSdwaOT5+vB86RJLPu/aaCIAgq0WzxYMzs7+nj/cCKtZRtWMjkOjES2Cl93gNYJpO3gqSnJd0v6asAmbDMJ0l6StI/JBUMC4YB7wKY2UxgIrBoqUYlHSLpCUlPTJ85tb5nFARBUI68ji67N2TyEpIuknR72l9T0sF5yja7gjkI+LGkJ3FfONNT+vvAsma2PnAkcFWan2nFHWw+ZGYbAI8AZ6QypbwIlPyazOxCM9vIzDYa0Dpf/c4mCIKgAgLUZrm2bmQ4Pno0NO2/Avw0T8GmVjBmNsrMtjGzDYGrca/MmNk0MyvEh34ypa+Kx4yeis/XAPwD2CB9Hk3qAUlqxSeqxnXTqQRBEORCZrm2bmSImV0HtMOsEaBcIZObWsFIWjz9bwGOBc5P+4sVwi9LWhFYBXgjzafcCmyZqtgKn5ACn5w6IH3eHbgn5l+CIGgqmnCIDPcvuWihVUmb4FMMVWmaSX5JV+OKYYik0fjioEGSfpwOuZEOLwCbAydKKmjSQ82s0Bv5JXC5pLOAj4HvpfSLUvpreM9lr8aeURAEQa10u4VYHo7EX9BXkvQQsBg96K6/S5jZ3mWyzi5x7A3ADWXqeRtXQMXpn+OGAkEQBE1LE1qRPSVpC9yqV8DLZjajSjGgyYfIgiAI+hxNsg5G0hclLeki2UxgQ+Bk4ExJi+SpIxRMEARBs2BNZUV2AclyV9LmwKnAZfj8Sy6nmU0zRBYEQRDQ3RP4leiXmdveE4+YeQNwg6Rn8lQQCqYK0xbsx5jN56V1w+UY9CY8/8ef9bRIQRD0YrrZBLkS/SS1puGxrYBDMnm5dEcomCoMnNjGcjePw/r3Q9Nnsv1qR2MDW2mffyD93h+HTZ4C7X5DqH8rtLZiiy3sY6QzPbi2ZnaYjN/+8qk9ch5BEMwlNI+CuRq4X9JYPGDZgwCSVmZuM1NuVj5fpB+v7bcwahNqB7X5Nur46MkEQVBnjLScsecxs5Ml3Q0sBYzIrBtsAX6Sp45QMFVYYeGPGPWLI3tajCAI+gCi21fpV8TMHi2R9kre8qFgqvDeO4vx9W+cSr+pM9GMdtTePqsLe+eTJ/SwdEEQ9Dram6QLUwdCwVShbaD4ZI2BzP9h/07pD1/78x6SKAiCXksTDZHVg1AwVWid2s7QOz6g/b33senTQS20zDsP2y3yfTTvPH7QwIEw0BWQtfjSIrW3++R/YVFUWzu3v3FGuWaCIAiAprIim2NCwVRh2kItvHbQkmBLeqjSNmiZAS3TQe0w8vSY7A+CoI6Eguk79JsGC71s9J/S+Ut/+LoYIguCoN40pbPLLhMKpgqtnxsLvTw1DXnBiP/9tqdFCoKgt2JAndzASFoNuDaTtCLwWzM7qy4N5CAUTBU6fdXhuS0IggZTrzkYM3sZWA8gxc96j45gjN1CKJgqtLS102/KNDSjDesntlvr151W5gNuVjhzJsxsAwlaUnTmln7+uT1zw1gyEUk30e1v/akbziIIgrmGxgyRbQW8nsKZdBuhYKpgLUIz2mDiJNTWBoMXgE8nwTwDXXG0tsK0adCWlE6/ftAumG9eVzzTZ0C/Flc+GTfbt7/3lx48qyAImhKj8wtpZYZIeiKzf6GZlfNyvBfu+qVbCQUTBEHQNNQ0yT/WzDaqdpCkAcBOwDFzIllXCAVThfb+LbQNmgcGpTUvLTDildN6VqggCHov9R8i2x54ysw+rHfF1QgFEwRB0CwY0Fb3pfx70wPDYxAKpiot09uhnxjxyG96WpQgCHo91mEIVAckzQd8A/hB3SqtgVAw1WgRbfO0stWWpwBw932/6mGBgiDo1dRxiMzMpgKL1q3CGgkFU412o99nMwAY8WgssgyCoIHUZkXW9ISCqcKqqy8ViiUIgu4jXMX0HV5+9YOeFiEIgr5EKJi+Q8u0mT0tQhAEfQWzjkXbvYBQMNVoaWG7dd2C7I5nT+phYYIg6PVEDyYIgiBoCKFg+g6rrDmUO56InksQBN2B9SorsnBAX4VRb37IJvue2dNiBEHQFzAwa8+1zQ1ED6YK/T5rY6EXJrLdem6qfMczJ/awREEQ9Grq7yqmxwgFUwUfIgulEgRBN2DmYT56CaFggiAImomY5A+CIAgagUUPpu/w6gvv9bQIQRD0GWoKONb0hIKphnpagCAI+gzh7LJvscqaw3pahCAI+ggGWLiK6TuMevNDNvvOmQycMJOW6f7FR0yYIAgagtU34FhPEwqmCu2tMGOQaJunPy0zWnnk6p/3tEhBEPRiLIbI+g5rLrMEj198ZE+LEQRBX6EX9WBkvchioRFImgS83NNydJEhwNieFqILzK1yQ8jeUzSL7MuZ2WJdLSzpDvxc8jDWzLbralvdQSiYKkh6wsw26mk5usLcKvvcKjeE7D3F3Cx7byacXQZBEAQNIRRMEARB0BBCwVTnwp4WYA6YW2WfW+WGkL2nmJtl77XEHEwQBEHQEKIHEwRBEDSEUDBBEARBQwgFUwZJ20l6WdJrko7uaXlKIektSc9LekbSEyltEUl3SXo1/V84c/wx6XxelrRtN8t6saSPJI3MpNUsq6QN0zm/JunPkhrujrSM7MdLei9d+2ck7dBssktaRtK9kl6S9IKkI1J601/3CrI3/XUPMphZbEUb0A94HVgRGAA8C6zZ03KVkPMtYEhR2unA0enz0cBp6fOa6TwGAiuk8+vXjbJuDmwAjJwTWYH/AZvifq5vB7bvIdmPB/6vxLFNIzuwFLBB+rwA8EqSr+mvewXZm/66x9axRQ+mNBsDr5nZG2Y2HbgG2LmHZcrLzsCl6fOlwC6Z9GvMbJqZvQm8hp9nt2BmDwDjipJrklXSUsBgM3vE/MlxWaZMd8tejqaR3czeN7On0udJwEvAMOaC615B9nI0jexBB6FgSjMMeDezP5rKN3dPYcAISU9KOiSlLWFm74P/SIHFU3oznlOtsg5Ln4vTe4rDJD2XhtAKw0xNKbuk5YH1gceYy657kewwF133vk4omNKUGqNtRnvuL5vZBsD2wI8lbV7h2LnlnKC8rM10DucBKwHrAe8DZ6b0ppNd0iDgBuCnZvZppUNLpDWb7HPNdQ9CwZRjNLBMZn9pYEwPyVIWMxuT/n8E3IQPeX2YhgVI/z9KhzfjOdUq6+j0uTi92zGzD82szczagb/RMdzYVLJL6o8/oK80sxtT8lxx3UvJPrdc98AJBVOax4FVJK0gaQCwF3BLD8vUCUnzS1qg8BnYBhiJy3lAOuwA4Ob0+RZgL0kDJa0ArIJPfvYkNcmahnMmSdokWQLtnynTrRQe0Ild8WsPTSR7auci4CUz+2Mmq+mveznZ54brHmToaSuDZt2AHXDLldeBX/e0PCXkWxG3mnkWeKEgI7AocDfwavq/SKbMr9P5vEw3W9IAV+NDGjPwt8qDuyIrsBH+UHkdOIfkjaIHZL8ceB54Dn+4LdVssgNfwYeDngOeSdsOc8N1ryB701/32Dq2cBUTBEEQNIQYIguCIAgaQiiYIAiCoCGEggmCIAgaQiiYIAiCoCGEggmCIAgaQiiYoE8iqS15431B0rOSjpTUkvK2lDQx47H3GUlbF5UbKekfkoZljvmgyNPvAEmTi9o9UNI5PXHOQdDdtPa0AEHQQ3xmZusBSFocuApYEDgu5T9oZt+qUu5KYM/M/vHAZDM7o3BweIYP+jLRgwn6POaudg7BnSjWohEeBFZujFRBMPcTPZggAMzsjTREVvAs/FVJz2QO2c3MXi/sSGrFnYzeUaXqeYvqWYQmczsUBI0iFEwQdJDtvZQbIssqjAdxf1mVmDWkBj4Hg7suCYJeTyiYIAAkrQi04Z6F16hwaCeFEQRBeWIOJujzSFoMOB84x8I5XxDUjejBBH2VwlBXf2Am7qU369K+eA7md2Z2ffeJFwRzP+FNOQiCIGgIMUQWBEEQNIRQMEEQBEFDCAUTBEEQNIRQMEEQBEFDCAUTBEEQNIRQMEEQBEFDCAUTBEEQNIT/D7JSQma+PXSpAAAAAElFTkSuQmCC\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": [
    "myvars = list(filtered_xarr.keys()) #filtered_xarr.name\n",
    "\n",
    "if len(myvars) == 1:\n",
    "    var = myvars[0]\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 {metadata[pc][\"depth_m_v1\"]}-{metadata[pc][\"depth_m_v2\"]}\\nSel/All={sel_outof_all}'\n",
    "\n",
    "    plotFilteredVar(filtered_xarr[var], title=title)\n",
    "\n",
    "else:\n",
    "    print(f\"There are multiple VARs available from the applied filtered: {myvars}\")\n",
    "    \n",
    "    for var in myvars:\n",
    "        \n",
    "        title = f'Platform={pc}, Var={var} (Individual Platform)\\nFilter: Year={year}, {bbox_key}, Depth Range {metadata[pc][\"depth_m_v1\"]}-{metadata[pc][\"depth_m_v2\"]}\\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_dict** for all Platforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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 --> data has been aligned already\n",
      "Platform: GS - Vertical min = 1.0 --> data has been aligned already\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",
    "        if metadata[pc]['depth_m_v1']==0: align_and_nan = True\n",
    "        else: align_and_nan = False\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=[metadata[pc][\"depth_newindex4xr_v1\"],\n",
    "                                                      metadata[pc][\"depth_newindex4xr_v2\"]])\n",
    "    \n",
    "# filtered_xarr_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PC AA\tFiltered Dims: TIME=357, DEPTH=2710\n",
      "PC GS\tFiltered Dims: TIME=49, DEPTH=3585\n",
      "PC GT\tFiltered Dims: TIME=158, DEPTH=2858\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": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P.Code (TIME, DEPTH)\n",
      "AA (357, 2710)\n",
      "GS (49, 3585)\n",
      "GT (158, 2858)\n",
      "max_depth_elements: 3585\n",
      "min_depth_elements: 2710\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": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reshaping using minimum DEPTH dimension (2710):\n",
      "(357, 2710) --> (357, 2710)\n",
      "(357, 2710) --> (357, 2710)\n",
      "(49, 3585) --> (49, 2710)\n",
      "(49, 3585) --> (49, 2710)\n",
      "(158, 2858) --> (158, 2710)\n",
      "(158, 2858) --> (158, 2710)\n"
     ]
    }
   ],
   "source": [
    "# Use min_depth_elements across available platforms: Reduce size to the minimum dimension \n",
    "print(f'Reshaping using minimum DEPTH 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": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reshaping using maximum DEPTH dimension (3585):\n",
      "(357, 2710) --> (357, 3585)\n",
      "(357, 2710) --> (357, 3585)\n",
      "(49, 3585) --> (49, 3585)\n",
      "(49, 3585) --> (49, 3585)\n",
      "(158, 2858) --> (158, 3585)\n",
      "(158, 2858) --> (158, 3585)\n"
     ]
    }
   ],
   "source": [
    "print(f'Reshaping using maximum DEPTH 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": 53,
   "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 {metadata[pc][\"depth_m_v1\"]}-{metadata[pc][\"depth_m_v2\"]}\\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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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