toc_report_feb_2019_part2.ipynb

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   "source": [
    "%matplotlib inline\n",
    "import nivapy3 as nivapy\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import seaborn as sn\n",
    "import matplotlib.pyplot as plt\n",
    "import toc_trends_analysis as resa2_trends\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\", message=\"Mean of empty slice\")\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connection successful.\n"
     ]
    }
   ],
   "source": [
    "# Connect to NIVABASE\n",
    "eng = nivapy.da.connect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TOC Thematic Report - February 2019 (Part 2: Annual trends)\n",
    "\n",
    "## 1. Get list of stations"
   ]
  },
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   "source": []
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   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "583 projects in the RESA database.\n"
     ]
    },
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   ],
   "source": [
    "# Select projects\n",
    "prj_grid = nivapy.da.select_resa_projects(eng)\n",
    "prj_grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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     "text": [
      "15\n"
     ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>project_id</th>\n",
       "      <th>project_number</th>\n",
       "      <th>project_name</th>\n",
       "      <th>contact_person</th>\n",
       "      <th>project_description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>1679</td>\n",
       "      <td>None</td>\n",
       "      <td>ICPWaters US</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>2120</td>\n",
       "      <td>None</td>\n",
       "      <td>ICPWaters NO</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>2140</td>\n",
       "      <td>None</td>\n",
       "      <td>ICPWaters CA</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>467</th>\n",
       "      <td>2923</td>\n",
       "      <td>None</td>\n",
       "      <td>ICPWaters UK</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>2943</td>\n",
       "      <td>None</td>\n",
       "      <td>ICPWaters FI</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>2944</td>\n",
       "      <td>None</td>\n",
       "      <td>ICPWaters SE</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>2986</td>\n",
       "      <td>None</td>\n",
       "      <td>ICPWaters CZ</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>2987</td>\n",
       "      <td>None</td>\n",
       "      <td>ICPWaters IT</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>2988</td>\n",
       "      <td>None</td>\n",
       "      <td>ICPWaters PL</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>2989</td>\n",
       "      <td>None</td>\n",
       "      <td>ICPWaters CH</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>2990</td>\n",
       "      <td>None</td>\n",
       "      <td>ICPWaters LV</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>2991</td>\n",
       "      <td>None</td>\n",
       "      <td>ICPWaters EE</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>518</th>\n",
       "      <td>3445</td>\n",
       "      <td>None</td>\n",
       "      <td>ICPWaters IE</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>4410</td>\n",
       "      <td>None</td>\n",
       "      <td>ICPWaters MD</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>1660</td>\n",
       "      <td>None</td>\n",
       "      <td>ICPWaters DE</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
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       "     project_id project_number  project_name contact_person  \\\n",
       "383        1679           None  ICPWaters US           None   \n",
       "424        2120           None  ICPWaters NO           None   \n",
       "425        2140           None  ICPWaters CA           None   \n",
       "467        2923           None  ICPWaters UK           None   \n",
       "468        2943           None  ICPWaters FI           None   \n",
       "469        2944           None  ICPWaters SE           None   \n",
       "474        2986           None  ICPWaters CZ           None   \n",
       "475        2987           None  ICPWaters IT           None   \n",
       "476        2988           None  ICPWaters PL           None   \n",
       "477        2989           None  ICPWaters CH           None   \n",
       "478        2990           None  ICPWaters LV           None   \n",
       "479        2991           None  ICPWaters EE           None   \n",
       "518        3445           None  ICPWaters IE           None   \n",
       "581        4410           None  ICPWaters MD           None   \n",
       "380        1660           None  ICPWaters DE           None   \n",
       "\n",
       "    project_description  \n",
       "383                None  \n",
       "424                None  \n",
       "425                None  \n",
       "467                None  \n",
       "468                None  \n",
       "469                None  \n",
       "474                None  \n",
       "475                None  \n",
       "476                None  \n",
       "477                None  \n",
       "478                None  \n",
       "479                None  \n",
       "518                None  \n",
       "581                None  \n",
       "380                None  "
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   "source": [
    "prj_df = prj_grid.get_selected_df()\n",
    "print (len(prj_df))\n",
    "prj_df"
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       "      <th>station_id</th>\n",
       "      <th>station_code</th>\n",
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       "      <th>0</th>\n",
       "      <td>221</td>\n",
       "      <td>BIE01</td>\n",
       "      <td>Birkenes</td>\n",
       "      <td>58.385426</td>\n",
       "      <td>8.241721</td>\n",
       "      <td>190.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12080</td>\n",
       "      <td>STE01</td>\n",
       "      <td>Storgama v. dam</td>\n",
       "      <td>59.052333</td>\n",
       "      <td>8.653606</td>\n",
       "      <td>587.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23505</td>\n",
       "      <td>DE01</td>\n",
       "      <td>Schwarzwald, Dürreychbach</td>\n",
       "      <td>48.750167</td>\n",
       "      <td>8.443167</td>\n",
       "      <td>730.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23506</td>\n",
       "      <td>DE02</td>\n",
       "      <td>Fichtelgebirge, Eger</td>\n",
       "      <td>50.085000</td>\n",
       "      <td>11.824500</td>\n",
       "      <td>664.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23507</td>\n",
       "      <td>DE03</td>\n",
       "      <td>Rothaargebirge, Elberndorfer Bach</td>\n",
       "      <td>50.987833</td>\n",
       "      <td>8.201333</td>\n",
       "      <td>540.0</td>\n",
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       "   station_id station_code                       station_name   latitude  \\\n",
       "0         221        BIE01                           Birkenes  58.385426   \n",
       "1       12080        STE01                    Storgama v. dam  59.052333   \n",
       "2       23505         DE01          Schwarzwald, Dürreychbach  48.750167   \n",
       "3       23506         DE02               Fichtelgebirge, Eger  50.085000   \n",
       "4       23507         DE03  Rothaargebirge, Elberndorfer Bach  50.987833   \n",
       "\n",
       "   longitude  altitude  \n",
       "0   8.241721     190.0  \n",
       "1   8.653606     587.0  \n",
       "2   8.443167     730.0  \n",
       "3  11.824500     664.0  \n",
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   ],
   "source": [
    "# Get stations\n",
    "stn_df = nivapy.da.select_resa_project_stations(prj_df, eng)\n",
    "print(len(stn_df))\n",
    "stn_df.head()"
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}
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}
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}
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}
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}
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}
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}
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}
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}
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}
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}
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</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x7f19c95209b0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Map\n",
    "nivapy.spatial.quickmap(stn_df, popup='station_code')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Calculate annual trends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# User input\n",
    "# Specify projects of interest\n",
    "proj_list = ['ICPWaters US', 'ICPWaters NO', 'ICPWaters CA',\n",
    "             'ICPWaters UK', 'ICPWaters FI', 'ICPWaters SE',\n",
    "             'ICPWaters CZ', 'ICPWaters IT', 'ICPWaters PL',\n",
    "             'ICPWaters CH', 'ICPWaters LV', 'ICPWaters EE',\n",
    "             'ICPWaters IE', 'ICPWaters MD', 'ICPWaters DE']\n",
    "\n",
    "# Specify results folder\n",
    "res_fold = (r'../../../Thematic_Trends_Report_2019/results')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 1990 to 2016"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting data from RESA2...\n",
      "    The database contains duplicate values for some station-date-parameter combinations.\n",
      "    Only the most recent values will be used, but you should check the repeated values are not errors.\n",
      "    The duplicated entries are returned in a separate dataframe.\n",
      "\n",
      "    Some stations have no relevant data in the period specified. Their IDs are returned in a separate dataframe.\n",
      "\n",
      "    Done.\n",
      "\n",
      "Converting units and applying sea-salt correction...\n",
      "    Done.\n",
      "\n",
      "Calculating statistics...\n",
      "    Data series for Al at site 23456 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 23457 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 23458 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 23460 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for TOC at site 23468 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for EH at site 23468 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4 at site 23468 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECl at site 23468 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ENO3 at site 23468 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4X at site 23468 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4_ECl at site 23468 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECa_EMg at site 23468 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECaX_EMgX at site 23468 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ANC at site 23468 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 23505 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 23506 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 23508 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 23509 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 23513 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for TOC at site 23514 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 23515 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 23517 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for TOC at site 23517 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ANC at site 23517 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 23518 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for TOC at site 23518 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 23519 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for TOC at site 23519 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for EH at site 23519 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4 at site 23519 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECl at site 23519 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ENO3 at site 23519 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4X at site 23519 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4_ECl at site 23519 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECa_EMg at site 23519 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECaX_EMgX at site 23519 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ANC at site 23519 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 23520 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 23522 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 23523 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for TOC at site 23523 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 23524 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for TOC at site 23524 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 23525 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 23526 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for TOC at site 23526 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for EH at site 23526 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4 at site 23526 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECl at site 23526 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ENO3 at site 23526 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4X at site 23526 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4_ECl at site 23526 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECa_EMg at site 23526 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECaX_EMgX at site 23526 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ANC at site 23526 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for TOC at site 23527 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 23528 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for TOC at site 23528 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ANC at site 23528 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 23529 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for TOC at site 23531 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 23532 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 23533 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 23534 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 23535 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 23537 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for TOC at site 23540 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 23546 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for TOC at site 23552 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for TOC at site 23556 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for TOC at site 23559 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for TOC at site 23562 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 23563 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for TOC at site 23563 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for TOC at site 23564 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 23565 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for TOC at site 23565 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 23566 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for TOC at site 23566 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 23567 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for TOC at site 23567 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for EH at site 23568 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4 at site 23568 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECl at site 23568 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ENO3 at site 23568 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4X at site 23568 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4_ECl at site 23568 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECa_EMg at site 23568 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECaX_EMgX at site 23568 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ANC at site 23568 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for EH at site 23569 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4 at site 23569 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECl at site 23569 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ENO3 at site 23569 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4X at site 23569 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4_ECl at site 23569 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECa_EMg at site 23569 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECaX_EMgX at site 23569 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ANC at site 23569 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 23570 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 23571 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 23572 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for TOC at site 23573 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for EH at site 23573 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4 at site 23573 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECl at site 23573 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ENO3 at site 23573 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4X at site 23573 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4_ECl at site 23573 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECa_EMg at site 23573 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECaX_EMgX at site 23573 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ANC at site 23573 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 23574 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 23584 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for TOC at site 23584 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for TOC at site 23585 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for EH at site 23586 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4 at site 23586 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECl at site 23586 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ENO3 at site 23586 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4X at site 23586 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4_ECl at site 23586 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECa_EMg at site 23586 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECaX_EMgX at site 23586 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ANC at site 23586 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for EH at site 23619 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for EH at site 23621 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for EH at site 23644 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 23646 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for EH at site 23646 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4 at site 23646 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECl at site 23646 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ENO3 at site 23646 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4X at site 23646 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4_ECl at site 23646 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECa_EMg at site 23646 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECaX_EMgX at site 23646 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ANC at site 23646 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for EH at site 23724 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for EH at site 23725 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for EH at site 23726 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for EH at site 23727 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for EH at site 23728 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 23729 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for EH at site 23729 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4 at site 23729 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECl at site 23729 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ENO3 at site 23729 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4X at site 23729 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4_ECl at site 23729 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECa_EMg at site 23729 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECaX_EMgX at site 23729 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ANC at site 23729 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for EH at site 23730 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for EH at site 23731 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for EH at site 23732 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for EH at site 23733 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for EH at site 29759 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4 at site 29759 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECl at site 29759 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ENO3 at site 29759 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4X at site 29759 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4_ECl at site 29759 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECa_EMg at site 29759 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECaX_EMgX at site 29759 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ANC at site 29759 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 33066 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for TOC at site 33066 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for EH at site 33066 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4 at site 33066 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECl at site 33066 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ENO3 at site 33066 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4X at site 33066 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4_ECl at site 33066 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECa_EMg at site 33066 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECaX_EMgX at site 33066 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ANC at site 33066 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for EH at site 37047 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4 at site 37047 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECl at site 37047 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ENO3 at site 37047 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4X at site 37047 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4_ECl at site 37047 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECa_EMg at site 37047 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECaX_EMgX at site 37047 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ANC at site 37047 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for EH at site 38545 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4 at site 38545 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECl at site 38545 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ENO3 at site 38545 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4X at site 38545 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4_ECl at site 38545 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECa_EMg at site 38545 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECaX_EMgX at site 38545 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ANC at site 38545 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for EH at site 38546 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4 at site 38546 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECl at site 38546 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ENO3 at site 38546 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4X at site 38546 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4_ECl at site 38546 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECa_EMg at site 38546 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECaX_EMgX at site 38546 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ANC at site 38546 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 38553 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for TOC at site 38553 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for EH at site 38553 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4 at site 38553 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECl at site 38553 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ENO3 at site 38553 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4X at site 38553 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4_ECl at site 38553 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECa_EMg at site 38553 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECaX_EMgX at site 38553 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ANC at site 38553 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for TOC at site 38554 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ANC at site 38554 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for EH at site 38555 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4 at site 38555 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECl at site 38555 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ENO3 at site 38555 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4X at site 38555 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4_ECl at site 38555 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECa_EMg at site 38555 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECaX_EMgX at site 38555 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 38556 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for TOC at site 38556 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4 at site 38556 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECl at site 38556 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4X at site 38556 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4_ECl at site 38556 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECa_EMg at site 38556 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECaX_EMgX at site 38556 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ANC at site 38556 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for TOC at site 38557 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for EH at site 38557 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4 at site 38557 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECl at site 38557 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ENO3 at site 38557 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4X at site 38557 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4_ECl at site 38557 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECa_EMg at site 38557 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECaX_EMgX at site 38557 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ANC at site 38557 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 38558 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for TOC at site 38558 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for TOC at site 38559 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ANC at site 38559 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 38560 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for TOC at site 38560 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for EH at site 38561 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4 at site 38561 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECl at site 38561 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ENO3 at site 38561 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4X at site 38561 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4_ECl at site 38561 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECa_EMg at site 38561 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECaX_EMgX at site 38561 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 38563 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for TOC at site 38563 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4 at site 38563 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECl at site 38563 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4X at site 38563 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4_ECl at site 38563 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECa_EMg at site 38563 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECaX_EMgX at site 38563 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ANC at site 38563 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 38564 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4 at site 38564 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECl at site 38564 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4X at site 38564 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4_ECl at site 38564 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECa_EMg at site 38564 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECaX_EMgX at site 38564 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ANC at site 38564 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 38565 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for TOC at site 38565 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4 at site 38565 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECl at site 38565 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4X at site 38565 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4_ECl at site 38565 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECa_EMg at site 38565 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECaX_EMgX at site 38565 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 38566 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4 at site 38566 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECl at site 38566 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4X at site 38566 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4_ECl at site 38566 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECa_EMg at site 38566 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECaX_EMgX at site 38566 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ANC at site 38566 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for Al at site 38567 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for TOC at site 38567 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4 at site 38567 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECl at site 38567 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4X at site 38567 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ESO4_ECl at site 38567 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECa_EMg at site 38567 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ECaX_EMgX at site 38567 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Data series for ANC at site 38567 has fewer than 10 non-null values. Significance estimates may be unreliable.\n",
      "    Done.\n",
      "\n",
      "Finished.\n"
     ]
    }
   ],
   "source": [
    "# Specify period of interest\n",
    "st_yr, end_yr = 1990, 2016\n",
    "\n",
    "# Build output paths\n",
    "plot_fold = os.path.join(res_fold, 'trends_plots_%s-%s' % (st_yr, end_yr))\n",
    "res_csv = os.path.join(res_fold, 'res_%s-%s.csv' % (st_yr, end_yr))\n",
    "dup_csv = os.path.join(res_fold, 'dup_%s-%s.csv' % (st_yr, end_yr))\n",
    "nd_csv = os.path.join(res_fold, 'nd_%s-%s.csv' % (st_yr, end_yr))\n",
    "\n",
    "# Run analysis \n",
    "res_df, dup_df, nd_df = resa2_trends.run_trend_analysis(proj_list, \n",
    "                                                        eng,\n",
    "                                                        st_yr=st_yr, \n",
    "                                                        end_yr=end_yr,\n",
    "                                                        plot=False, \n",
    "                                                        fold=plot_fold)\n",
    "\n",
    "# Delete mk_std_dev col as not relevant here\n",
    "del res_df['mk_std_dev']\n",
    "\n",
    "# Write output\n",
    "res_df.to_csv(res_csv, index=False)\n",
    "dup_df.to_csv(dup_csv, index=False)\n",
    "if nd_df is not None:\n",
    "    nd_df.to_csv(nd_csv, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are lots of warnings printed above, but the main one of interest is:\n",
    "\n",
    "    Some stations have no relevant data in the period specified.\n",
    "    \n",
    "Which station(s) are missing data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>station_id</th>\n",
       "      <th>station_code</th>\n",
       "      <th>station_name</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>altitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>38562</td>\n",
       "      <td>IE20</td>\n",
       "      <td>Clare, Naminna, Mid Lake</td>\n",
       "      <td>52.78408</td>\n",
       "      <td>-9.22064</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     station_id station_code              station_name  latitude  longitude  \\\n",
       "251       38562         IE20  Clare, Naminna, Mid Lake  52.78408   -9.22064   \n",
       "\n",
       "     altitude  \n",
       "251       NaN  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get stations with no data\n",
    "stn_df[stn_df['station_id'].isin(nd_df['station_id'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems that one Irish station has no associated data. This is as expected, because all the data supplied by Julian for this site comes from \"near-shore\" sampling (rather than \"open water\") and these have been omitted from the data upload - see [here](https://nbviewer.jupyter.org/github/JamesSample/icpw/blob/master/toc_report_feb_2019_part1.ipynb#1.13.-Ireland) for details."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Basic checking\n",
    "\n",
    "### 3.1. Boxplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set up plot\n",
    "fig = plt.figure(figsize=(20,10))\n",
    "sn.set(style=\"ticks\", palette=\"muted\", \n",
    "       color_codes=True, font_scale=2)\n",
    "\n",
    "# Horizontal boxplots\n",
    "ax = sn.boxplot(x=\"mean\", y=\"par_id\", data=res_df,\n",
    "                whis=np.inf, color=\"c\")\n",
    "\n",
    "# Add \"raw\" data points for each observation, with some \"jitter\"\n",
    "# to make them visible\n",
    "sn.stripplot(x=\"mean\", y=\"par_id\", data=res_df, jitter=True, \n",
    "             size=3, color=\".3\", linewidth=0)\n",
    "\n",
    "# Remove axis lines\n",
    "sn.despine(trim=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Data restructuring\n",
    "\n",
    "The code below is taken from [here](https://nbviewer.jupyter.org/github/JamesSample/icpw/blob/master/toc_trends_oct_2016_part2.ipynb#7.-Data-restructuring). It is used to generate output files in the format requested by Heleen.\n",
    "\n",
    "### 4.1. Combine datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>station_id</th>\n",
       "      <th>station_code</th>\n",
       "      <th>station_name</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>analysis_period</th>\n",
       "      <th>data_period</th>\n",
       "      <th>par_id</th>\n",
       "      <th>non_missing</th>\n",
       "      <th>n_start</th>\n",
       "      <th>n_end</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>std_dev</th>\n",
       "      <th>mk_stat</th>\n",
       "      <th>norm_mk_stat</th>\n",
       "      <th>mk_p_val</th>\n",
       "      <th>trend</th>\n",
       "      <th>sen_slp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>108</td>\n",
       "      <td>LAE01</td>\n",
       "      <td>Langtjern, utlÃ¸p</td>\n",
       "      <td>60.37246</td>\n",
       "      <td>9.72666</td>\n",
       "      <td>1990-2016</td>\n",
       "      <td>1990-2016</td>\n",
       "      <td>Al</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>383.392409</td>\n",
       "      <td>383.392409</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>108</td>\n",
       "      <td>LAE01</td>\n",
       "      <td>Langtjern, utlÃ¸p</td>\n",
       "      <td>60.37246</td>\n",
       "      <td>9.72666</td>\n",
       "      <td>1990-2016</td>\n",
       "      <td>1990-2016</td>\n",
       "      <td>TOC</td>\n",
       "      <td>27</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>10.930741</td>\n",
       "      <td>11.200000</td>\n",
       "      <td>1.172784</td>\n",
       "      <td>107.0</td>\n",
       "      <td>2.214912</td>\n",
       "      <td>2.676609e-02</td>\n",
       "      <td>increasing</td>\n",
       "      <td>0.055556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>108</td>\n",
       "      <td>LAE01</td>\n",
       "      <td>Langtjern, utlÃ¸p</td>\n",
       "      <td>60.37246</td>\n",
       "      <td>9.72666</td>\n",
       "      <td>1990-2016</td>\n",
       "      <td>1990-2016</td>\n",
       "      <td>EH</td>\n",
       "      <td>27</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>10.163628</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.511132</td>\n",
       "      <td>-120.0</td>\n",
       "      <td>-2.483843</td>\n",
       "      <td>1.299732e-02</td>\n",
       "      <td>decreasing</td>\n",
       "      <td>-0.153421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>108</td>\n",
       "      <td>LAE01</td>\n",
       "      <td>Langtjern, utlÃ¸p</td>\n",
       "      <td>60.37246</td>\n",
       "      <td>9.72666</td>\n",
       "      <td>1990-2016</td>\n",
       "      <td>1990-2016</td>\n",
       "      <td>ESO4</td>\n",
       "      <td>27</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>30.461034</td>\n",
       "      <td>24.166667</td>\n",
       "      <td>15.724514</td>\n",
       "      <td>-306.0</td>\n",
       "      <td>-6.359690</td>\n",
       "      <td>2.021618e-10</td>\n",
       "      <td>decreasing</td>\n",
       "      <td>-1.739583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>108</td>\n",
       "      <td>LAE01</td>\n",
       "      <td>Langtjern, utlÃ¸p</td>\n",
       "      <td>60.37246</td>\n",
       "      <td>9.72666</td>\n",
       "      <td>1990-2016</td>\n",
       "      <td>1990-2016</td>\n",
       "      <td>ECl</td>\n",
       "      <td>27</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>13.823280</td>\n",
       "      <td>12.857143</td>\n",
       "      <td>2.809527</td>\n",
       "      <td>-163.0</td>\n",
       "      <td>-3.421093</td>\n",
       "      <td>6.236991e-04</td>\n",
       "      <td>decreasing</td>\n",
       "      <td>-0.190476</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   station_id station_code       station_name  latitude  longitude  \\\n",
       "0         108        LAE01  Langtjern, utlÃ¸p  60.37246    9.72666   \n",
       "1         108        LAE01  Langtjern, utlÃ¸p  60.37246    9.72666   \n",
       "2         108        LAE01  Langtjern, utlÃ¸p  60.37246    9.72666   \n",
       "3         108        LAE01  Langtjern, utlÃ¸p  60.37246    9.72666   \n",
       "4         108        LAE01  Langtjern, utlÃ¸p  60.37246    9.72666   \n",
       "\n",
       "  analysis_period data_period par_id  non_missing  n_start  n_end        mean  \\\n",
       "0       1990-2016   1990-2016     Al            1        0      0  383.392409   \n",
       "1       1990-2016   1990-2016    TOC           27        5      5   10.930741   \n",
       "2       1990-2016   1990-2016     EH           27        5      5   10.163628   \n",
       "3       1990-2016   1990-2016   ESO4           27        5      5   30.461034   \n",
       "4       1990-2016   1990-2016    ECl           27        5      5   13.823280   \n",
       "\n",
       "       median    std_dev  mk_stat  norm_mk_stat      mk_p_val       trend  \\\n",
       "0  383.392409        NaN      NaN           NaN           NaN         NaN   \n",
       "1   11.200000   1.172784    107.0      2.214912  2.676609e-02  increasing   \n",
       "2   10.000000   2.511132   -120.0     -2.483843  1.299732e-02  decreasing   \n",
       "3   24.166667  15.724514   -306.0     -6.359690  2.021618e-10  decreasing   \n",
       "4   12.857143   2.809527   -163.0     -3.421093  6.236991e-04  decreasing   \n",
       "\n",
       "    sen_slp  \n",
       "0       NaN  \n",
       "1  0.055556  \n",
       "2 -0.153421  \n",
       "3 -1.739583  \n",
       "4 -0.190476  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Change 'period' col to 'data_period' and add 'analysis_period'\n",
    "res_df['data_period'] = res_df['period']\n",
    "del res_df['period']\n",
    "\n",
    "res_df['analysis_period'] = '1990-2016'\n",
    "\n",
    "# Join\n",
    "df = pd.merge(res_df, stn_df, how='left', on='station_id')\n",
    "\n",
    "# Re-order columns\n",
    "df = df[['station_id', \n",
    "         'station_code', 'station_name', \n",
    "         'latitude', 'longitude', 'analysis_period', 'data_period', \n",
    "         'par_id', 'non_missing', 'n_start', 'n_end', 'mean', 'median', \n",
    "         'std_dev', 'mk_stat', 'norm_mk_stat', 'mk_p_val', 'trend',\n",
    "         'sen_slp']]\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.2. Check record completeness\n",
    "\n",
    "See e-mail from Heleen received 25/10/2016 at 15:56. The `'non_missing'` threshold is based of 65% of the data period (e.g. 65% of 27 years for 1990 to 2016)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def include(row):\n",
    "    if ((row['analysis_period'] == '1990-2016') & \n",
    "        (row['n_start'] >= 2) &\n",
    "        (row['n_end'] >= 2) &\n",
    "        (row['non_missing'] >= 18)):\n",
    "        return 'yes'\n",
    "    elif ((row['analysis_period'] == '1990-2004') & \n",
    "          (row['n_start'] >= 2) &\n",
    "          (row['n_end'] >= 2) &          \n",
    "          (row['non_missing'] >= 10)):\n",
    "        return 'yes'\n",
    "    elif ((row['analysis_period'] == '2002-2016') & \n",
    "          (row['n_start'] >= 2) &\n",
    "          (row['n_end'] >= 2) &          \n",
    "          (row['non_missing'] >= 10)):\n",
    "        return 'yes'\n",
    "    else:\n",
    "        return 'no'\n",
    "\n",
    "df['include'] = df.apply(include, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3. SO4 at Abiskojaure\n",
    "\n",
    "SO4 for this station (`'station_id=36458'` in the \"core\" dataset) should be removed. See [here](https://nbviewer.jupyter.org/github/JamesSample/icpw/blob/master/toc_trends_oct_2016_part3.ipynb#1.3.-SO4-for-station-ID-36561)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove sulphate-related series at Abiskojaure\n",
    "df = df.query('not((station_id==36458) and ((par_id==\"ESO4\") or '\n",
    "                                           '(par_id==\"ESO4X\") or '\n",
    "                                           '(par_id==\"ESO4_ECl\")))')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.4. Relative slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Relative slope\n",
    "df['rel_sen_slp'] = df['sen_slp'] / df['median'] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.5. Tidy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>station_id</th>\n",
       "      <th>station_code</th>\n",
       "      <th>station_name</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>analysis_period</th>\n",
       "      <th>data_period</th>\n",
       "      <th>par_id</th>\n",
       "      <th>non_missing</th>\n",
       "      <th>median</th>\n",
       "      <th>std_dev</th>\n",
       "      <th>mk_p_val</th>\n",
       "      <th>trend</th>\n",
       "      <th>sen_slp</th>\n",
       "      <th>rel_sen_slp</th>\n",
       "      <th>include</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>108</td>\n",
       "      <td>LAE01</td>\n",
       "      <td>Langtjern, utlÃ¸p</td>\n",
       "      <td>60.37246</td>\n",
       "      <td>9.72666</td>\n",
       "      <td>1990-2016</td>\n",
       "      <td>1990-2016</td>\n",
       "      <td>Al</td>\n",
       "      <td>1</td>\n",
       "      <td>383.392409</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>108</td>\n",
       "      <td>LAE01</td>\n",
       "      <td>Langtjern, utlÃ¸p</td>\n",
       "      <td>60.37246</td>\n",
       "      <td>9.72666</td>\n",
       "      <td>1990-2016</td>\n",
       "      <td>1990-2016</td>\n",
       "      <td>TOC</td>\n",
       "      <td>27</td>\n",
       "      <td>11.200000</td>\n",
       "      <td>1.172784</td>\n",
       "      <td>2.676609e-02</td>\n",
       "      <td>increasing</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.004960</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>108</td>\n",
       "      <td>LAE01</td>\n",
       "      <td>Langtjern, utlÃ¸p</td>\n",
       "      <td>60.37246</td>\n",
       "      <td>9.72666</td>\n",
       "      <td>1990-2016</td>\n",
       "      <td>1990-2016</td>\n",
       "      <td>EH</td>\n",
       "      <td>27</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.511132</td>\n",
       "      <td>1.299732e-02</td>\n",
       "      <td>decreasing</td>\n",
       "      <td>-0.153421</td>\n",
       "      <td>-0.015342</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>108</td>\n",
       "      <td>LAE01</td>\n",
       "      <td>Langtjern, utlÃ¸p</td>\n",
       "      <td>60.37246</td>\n",
       "      <td>9.72666</td>\n",
       "      <td>1990-2016</td>\n",
       "      <td>1990-2016</td>\n",
       "      <td>ESO4</td>\n",
       "      <td>27</td>\n",
       "      <td>24.166667</td>\n",
       "      <td>15.724514</td>\n",
       "      <td>2.021618e-10</td>\n",
       "      <td>decreasing</td>\n",
       "      <td>-1.739583</td>\n",
       "      <td>-0.071983</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>108</td>\n",
       "      <td>LAE01</td>\n",
       "      <td>Langtjern, utlÃ¸p</td>\n",
       "      <td>60.37246</td>\n",
       "      <td>9.72666</td>\n",
       "      <td>1990-2016</td>\n",
       "      <td>1990-2016</td>\n",
       "      <td>ECl</td>\n",
       "      <td>27</td>\n",
       "      <td>12.857143</td>\n",
       "      <td>2.809527</td>\n",
       "      <td>6.236991e-04</td>\n",
       "      <td>decreasing</td>\n",
       "      <td>-0.190476</td>\n",
       "      <td>-0.014815</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   station_id station_code       station_name  latitude  longitude  \\\n",
       "0         108        LAE01  Langtjern, utlÃ¸p  60.37246    9.72666   \n",
       "1         108        LAE01  Langtjern, utlÃ¸p  60.37246    9.72666   \n",
       "2         108        LAE01  Langtjern, utlÃ¸p  60.37246    9.72666   \n",
       "3         108        LAE01  Langtjern, utlÃ¸p  60.37246    9.72666   \n",
       "4         108        LAE01  Langtjern, utlÃ¸p  60.37246    9.72666   \n",
       "\n",
       "  analysis_period data_period par_id  non_missing      median    std_dev  \\\n",
       "0       1990-2016   1990-2016     Al            1  383.392409        NaN   \n",
       "1       1990-2016   1990-2016    TOC           27   11.200000   1.172784   \n",
       "2       1990-2016   1990-2016     EH           27   10.000000   2.511132   \n",
       "3       1990-2016   1990-2016   ESO4           27   24.166667  15.724514   \n",
       "4       1990-2016   1990-2016    ECl           27   12.857143   2.809527   \n",
       "\n",
       "       mk_p_val       trend   sen_slp  rel_sen_slp include  \n",
       "0           NaN         NaN       NaN          NaN      no  \n",
       "1  2.676609e-02  increasing  0.055556     0.004960     yes  \n",
       "2  1.299732e-02  decreasing -0.153421    -0.015342     yes  \n",
       "3  2.021618e-10  decreasing -1.739583    -0.071983     yes  \n",
       "4  6.236991e-04  decreasing -0.190476    -0.014815     yes  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove unwanted cols\n",
    "df.drop(labels=['mean', 'n_end', 'n_start', 'mk_stat', 'norm_mk_stat'],\n",
    "        axis=1, inplace=True)\n",
    "\n",
    "# Reorder columns\n",
    "df = df[['station_id', 'station_code', \n",
    "         'station_name', 'latitude', 'longitude', 'analysis_period',\n",
    "         'data_period', 'par_id', 'non_missing', 'median', 'std_dev',\n",
    "         'mk_p_val', 'trend', 'sen_slp', 'rel_sen_slp', 'include']]\n",
    "\n",
    "# Write to output\n",
    "out_path = os.path.join(res_fold, 'toc_core_trends_long_format.csv')\n",
    "df.to_csv(out_path, index=False, encoding='utf-8')\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.6. Convert to \"wide\" format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>station_id</th>\n",
       "      <th>station_code</th>\n",
       "      <th>station_name</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>TOC_1990-2016_non_missing</th>\n",
       "      <th>EH_1990-2016_non_missing</th>\n",
       "      <th>ESO4_1990-2016_non_missing</th>\n",
       "      <th>ECl_1990-2016_non_missing</th>\n",
       "      <th>ENO3_1990-2016_non_missing</th>\n",
       "      <th>...</th>\n",
       "      <th>EH_1990-2016_rel_sen_slp</th>\n",
       "      <th>ESO4_1990-2016_rel_sen_slp</th>\n",
       "      <th>ECl_1990-2016_rel_sen_slp</th>\n",
       "      <th>ENO3_1990-2016_rel_sen_slp</th>\n",
       "      <th>ESO4X_1990-2016_rel_sen_slp</th>\n",
       "      <th>ESO4_ECl_1990-2016_rel_sen_slp</th>\n",
       "      <th>ECa_EMg_1990-2016_rel_sen_slp</th>\n",
       "      <th>ECaX_EMgX_1990-2016_rel_sen_slp</th>\n",
       "      <th>ANC_1990-2016_rel_sen_slp</th>\n",
       "      <th>Al_1990-2016_rel_sen_slp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>108</td>\n",
       "      <td>LAE01</td>\n",
       "      <td>Langtjern, utlÃ¸p</td>\n",
       "      <td>60.372460</td>\n",
       "      <td>9.726660</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0153421</td>\n",
       "      <td>-0.0719828</td>\n",
       "      <td>-0.0148148</td>\n",
       "      <td>-0.0366133</td>\n",
       "      <td>-0.0752522</td>\n",
       "      <td>-0.0537174</td>\n",
       "      <td>-0.0176009</td>\n",
       "      <td>-0.0175883</td>\n",
       "      <td>0.015666</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>221</td>\n",
       "      <td>BIE01</td>\n",
       "      <td>Birkenes</td>\n",
       "      <td>58.385426</td>\n",
       "      <td>8.241721</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0235381</td>\n",
       "      <td>-0.0413194</td>\n",
       "      <td>-0.00737492</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.0494009</td>\n",
       "      <td>-0.0203906</td>\n",
       "      <td>-0.0241935</td>\n",
       "      <td>-0.0387372</td>\n",
       "      <td>-0.197483</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12080</td>\n",
       "      <td>STE01</td>\n",
       "      <td>Storgama v. dam</td>\n",
       "      <td>59.052333</td>\n",
       "      <td>8.653606</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0425499</td>\n",
       "      <td>-0.0769737</td>\n",
       "      <td>-0.0101838</td>\n",
       "      <td>-0.118056</td>\n",
       "      <td>-0.0872979</td>\n",
       "      <td>-0.0427573</td>\n",
       "      <td>-0.0167173</td>\n",
       "      <td>-0.018849</td>\n",
       "      <td>0.154454</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12081</td>\n",
       "      <td>KAE01</td>\n",
       "      <td>KÃ¥rvatn feltforskningsstasjon</td>\n",
       "      <td>62.782883</td>\n",
       "      <td>8.893443</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0191471</td>\n",
       "      <td>-0.0166667</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.0304348</td>\n",
       "      <td>-0.0249274</td>\n",
       "      <td>-0.00537025</td>\n",
       "      <td>0.00480173</td>\n",
       "      <td>0.00750062</td>\n",
       "      <td>0.0177619</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12082</td>\n",
       "      <td>DALELV</td>\n",
       "      <td>Dalelv</td>\n",
       "      <td>69.684738</td>\n",
       "      <td>30.386154</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0250379</td>\n",
       "      <td>-0.0236446</td>\n",
       "      <td>-0.00325815</td>\n",
       "      <td>-0.0404762</td>\n",
       "      <td>-0.0277961</td>\n",
       "      <td>-0.00997677</td>\n",
       "      <td>-0.00561964</td>\n",
       "      <td>-0.00654937</td>\n",
       "      <td>0.0249325</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 82 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   station_id station_code                    station_name   latitude  \\\n",
       "0         108        LAE01               Langtjern, utlÃ¸p  60.372460   \n",
       "1         221        BIE01                        Birkenes  58.385426   \n",
       "2       12080        STE01                 Storgama v. dam  59.052333   \n",
       "3       12081        KAE01  KÃ¥rvatn feltforskningsstasjon  62.782883   \n",
       "4       12082       DALELV                          Dalelv  69.684738   \n",
       "\n",
       "   longitude TOC_1990-2016_non_missing EH_1990-2016_non_missing  \\\n",
       "0   9.726660                        27                       27   \n",
       "1   8.241721                        27                       27   \n",
       "2   8.653606                        27                       27   \n",
       "3   8.893443                        27                       27   \n",
       "4  30.386154                        27                       27   \n",
       "\n",
       "  ESO4_1990-2016_non_missing ECl_1990-2016_non_missing  \\\n",
       "0                         27                        27   \n",
       "1                         27                        27   \n",
       "2                         27                        27   \n",
       "3                         27                        27   \n",
       "4                         27                        27   \n",
       "\n",
       "  ENO3_1990-2016_non_missing           ...             \\\n",
       "0                         27           ...              \n",
       "1                         27           ...              \n",
       "2                         27           ...              \n",
       "3                         27           ...              \n",
       "4                         27           ...              \n",
       "\n",
       "  EH_1990-2016_rel_sen_slp ESO4_1990-2016_rel_sen_slp  \\\n",
       "0               -0.0153421                 -0.0719828   \n",
       "1               -0.0235381                 -0.0413194   \n",
       "2               -0.0425499                 -0.0769737   \n",
       "3               -0.0191471                 -0.0166667   \n",
       "4               -0.0250379                 -0.0236446   \n",
       "\n",
       "  ECl_1990-2016_rel_sen_slp ENO3_1990-2016_rel_sen_slp  \\\n",
       "0                -0.0148148                 -0.0366133   \n",
       "1               -0.00737492                      -0.01   \n",
       "2                -0.0101838                  -0.118056   \n",
       "3                         0                 -0.0304348   \n",
       "4               -0.00325815                 -0.0404762   \n",
       "\n",
       "  ESO4X_1990-2016_rel_sen_slp ESO4_ECl_1990-2016_rel_sen_slp  \\\n",
       "0                  -0.0752522                     -0.0537174   \n",
       "1                  -0.0494009                     -0.0203906   \n",
       "2                  -0.0872979                     -0.0427573   \n",
       "3                  -0.0249274                    -0.00537025   \n",
       "4                  -0.0277961                    -0.00997677   \n",
       "\n",
       "  ECa_EMg_1990-2016_rel_sen_slp ECaX_EMgX_1990-2016_rel_sen_slp  \\\n",
       "0                    -0.0176009                      -0.0175883   \n",
       "1                    -0.0241935                      -0.0387372   \n",
       "2                    -0.0167173                       -0.018849   \n",
       "3                    0.00480173                      0.00750062   \n",
       "4                   -0.00561964                     -0.00654937   \n",
       "\n",
       "  ANC_1990-2016_rel_sen_slp Al_1990-2016_rel_sen_slp  \n",
       "0                  0.015666                      NaN  \n",
       "1                 -0.197483                      NaN  \n",
       "2                  0.154454                      NaN  \n",
       "3                 0.0177619                      NaN  \n",
       "4                 0.0249325                      NaN  \n",
       "\n",
       "[5 rows x 82 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del df['data_period']\n",
    "\n",
    "# Melt to \"long\" format\n",
    "melt_df = pd.melt(df, \n",
    "                  id_vars=['station_id', 'station_code', \n",
    "                           'station_name', 'latitude', 'longitude', \n",
    "                           'analysis_period', 'par_id', 'include'],\n",
    "                  var_name='stat')\n",
    "\n",
    "# Get only values where include='yes'\n",
    "melt_df = melt_df.query('include == \"yes\"')\n",
    "del melt_df['include']\n",
    "\n",
    "# Build multi-index on everything except \"value\"\n",
    "melt_df.set_index(['station_id', 'station_code', \n",
    "                   'station_name', 'latitude', 'longitude', 'par_id', \n",
    "                   'analysis_period', \n",
    "                   'stat'], inplace=True)\n",
    "\n",
    "# Unstack levels of interest to columns\n",
    "wide_df = melt_df.unstack(level=['par_id', 'analysis_period', 'stat'])\n",
    "\n",
    "# Drop unwanted \"value\" level in index\n",
    "wide_df.columns = wide_df.columns.droplevel(0)\n",
    "\n",
    "# Replace multi-index with separate components concatenated with '_'\n",
    "wide_df.columns = [\"_\".join(item) for item in wide_df.columns]\n",
    "\n",
    "# Reset multiindex on rows\n",
    "wide_df = wide_df.reset_index()\n",
    "\n",
    "# Save output\n",
    "out_path = os.path.join(res_fold, 'toc_trends_wide_format.csv')\n",
    "wide_df.to_csv(out_path, index=False, encoding='utf-8')\n",
    "\n",
    "wide_df.head()"
   ]
  }
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