profiles.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting Oceanographic Station Data, modified from Deepa Rao!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os #need to to move between directories while using jupyter notebook\n",
    "import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data & math\n",
    "import math\n",
    "from math import e\n",
    "import scipy as sp\n",
    "from scipy import stats\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "\n",
    "# open CNV files for CTD data\n",
    "from seabird.cnv import fCNV\n",
    "\n",
    "# seawater property calculations\n",
    "#import seawater as sw #seawater package: https://pypi.org/project/seawater/, examples: https://pythonhosted.org/seawater/library.html\n",
    "\n",
    "# plotting\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm #colormap\n",
    "\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "#import cmocean as cmo\n",
    "\n",
    "# set seaborn style: http://www.jesshamrick.com/2016/04/13/reproducible-plots/\n",
    "sns.set_context('paper')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Data from CTD + metal quant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load CTD files from directory\n",
    "ctd_st1 = fCNV('CTD/BV55C2.cnv')\n",
    "ctd_st2 = fCNV('CTD/BV55C5.cnv')\n",
    "ctd_st3 = fCNV('CTD/BV55C7.cnv')\n",
    "ctd_st5 = fCNV('CTD/BV55C9.cnv')\n",
    "ctd_st6 = fCNV('CTD/BV55C11.cnv')\n",
    "ctd_st7 = fCNV('CTD/BV55C16.cnv')\n",
    "ctd_st8 = fCNV('CTD/BV55C17.cnv')\n",
    "\n",
    "\n",
    "# read CNV files into DF\n",
    "ctd_st1 = ctd_st1.as_DataFrame()\n",
    "ctd_st2 = ctd_st2.as_DataFrame()\n",
    "ctd_st3 = ctd_st3.as_DataFrame()\n",
    "ctd_st5 = ctd_st5.as_DataFrame()\n",
    "ctd_st6 = ctd_st6.as_DataFrame()\n",
    "ctd_st7 = ctd_st7.as_DataFrame()\n",
    "ctd_st8 = ctd_st8.as_DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dissolved Fe, particulate Fe, O2 and CHL\n",
    "plt.rcParams[\"font.family\"] = \"serif\"\n",
    "\n",
    "odv = pd.read_csv('metals_ODV.csv')\n",
    "\n",
    "#subset combined datasets for each station of interest\n",
    "stn1 = odv.loc[odv['Station'] == 1]\n",
    "stn2 = odv.loc[odv['Station'] == 2]\n",
    "stn3 = odv.loc[odv['Station'] == 3]\n",
    "stn5 = odv.loc[odv['Station'] == 5]\n",
    "stn6 = odv.loc[odv['Station'] == 6]\n",
    "stn7 = odv.loc[odv['Station'] == 7]\n",
    "stn8 = odv.loc[odv['Station'] == 8]\n",
    "stn9 = odv.loc[odv['Station'] == 9]\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(3,5))\n",
    "font_size = 16\n",
    "ax1 = fig.add_subplot(111) \n",
    "ax2 = ax1.twiny() #define second x-axis\n",
    "ax3 = ax1.twiny() #define second x-axis\n",
    "ax4 = ax1.twiny() #define second x-axis\n",
    "\n",
    "#data\n",
    "df = stn9\n",
    "ctd = ctd_st8 #St. 9 has no CTD\n",
    "\n",
    "y1 = df['depth'] \n",
    "y2 = ctd['PRES']\n",
    "x1 = df['pFe (pM)'] \n",
    "x2 = df['Fe (nM)'] \n",
    "x3 = ctd['sbeox0Mm/L'] \n",
    "x4 = ctd['flC']\n",
    "\n",
    "#main data colors\n",
    "x1_color = 'coral' \n",
    "x2_color = 'brown'\n",
    "x3_color = 'midnightblue'\n",
    "x4_color = 'darkturquoise'\n",
    "\n",
    "#LOD for dissolved Fe\n",
    "cond = x2 < 0.3\n",
    "\n",
    "# plot\n",
    "fig1, = ax1.plot( x1, y1, 'o--', color=x1_color, linewidth=2, markersize=8, data=df, label='pFe (pM)')\n",
    "fig2, = ax2.plot( x2, y1, 'o-', color=x2_color, linewidth=2, markersize=8, data=df, label='dFe (nM)')\n",
    "fig2, = ax2.plot( x2[cond], y1[cond], 'o', color='#D3D3D3', linewidth=2, markersize=8, data=df, label='dFe (nM)')\n",
    "fig3, = ax3.plot( x3, y2, 'o--', color=x3_color, linewidth=2, markersize=0, data=ctd, label='O$_{2}$ ($ \\mu$M)')\n",
    "fig4, = ax4.plot( x4, y2, 'o--', color=x4_color, linewidth=2, markersize=0, data=ctd, label='Fluorescence ($ \\mu$g/L)')\n",
    "\n",
    "\n",
    "#alter y axis\n",
    "ax1.set_ylim([0,1500])\n",
    "ax2.set_ylim([0,1500])\n",
    "ax3.set_ylim([0,1500])\n",
    "ax4.set_ylim([0,1500])\n",
    "ax1.invert_yaxis()\n",
    "\n",
    "#alter y axis\n",
    "ax1.set_xlim([0,500])\n",
    "ax2.set_xlim([0,2])\n",
    "ax3.set_xlim([0,300])\n",
    "ax4.set_xlim([0,3])\n",
    "\n",
    "#St. 8\n",
    "#ax1.set_xlim([0,600])\n",
    "#ax2.set_xlim([0,5])\n",
    "#ax3.set_xlim([0,300])\n",
    "#ax4.set_xlim([0,8])\n",
    "\n",
    "#axes labels\n",
    "ax1.set_ylabel('Depth (m)', fontsize=font_size)\n",
    "ax1.set_xlabel('pFe (pM)', color=x1_color, fontsize=font_size)\n",
    "ax2.set_xlabel('dFe (nM)', color=x2_color, fontsize=font_size)\n",
    "ax3.set_xlabel('O$_{2}$ ($ \\mu$M)', color=x3_color, fontsize=font_size)\n",
    "ax4.set_xlabel('Fluorescence ($ \\mu$g/L)', color=x4_color, fontsize=font_size)\n",
    "\n",
    "#axis location and details\n",
    "ax1.spines['bottom'].set_position(('outward', 0))\n",
    "ax2.spines['top'].set_position(('outward', 0))\n",
    "ax3.spines['top'].set_position(('outward', 50))\n",
    "ax4.spines['bottom'].set_position(('outward', 50))\n",
    "\n",
    "ax1.tick_params(axis='y', colors='black', labelsize=12)\n",
    "ax1.tick_params(axis='x', colors='black', labelsize=12)\n",
    "ax1.tick_params(axis='x', colors='black', labelsize=12)\n",
    "ax2.tick_params(axis='x', colors='black', labelsize=12)\n",
    "ax3.tick_params(axis='x', colors='black', labelsize=12)\n",
    "ax4.tick_params(axis='x', colors='black', labelsize=12)\n",
    "\n",
    "ax1.xaxis.set_ticks_position('bottom')\n",
    "ax2.xaxis.set_ticks_position('top')\n",
    "ax3.xaxis.set_ticks_position('top')\n",
    "ax4.xaxis.set_ticks_position('bottom')\n",
    "\n",
    "ax1.xaxis.set_label_position('bottom')\n",
    "ax2.xaxis.set_label_position('top')\n",
    "ax3.xaxis.set_label_position('top')\n",
    "ax4.xaxis.set_label_position('bottom')\n",
    "\n",
    "ax1.text(0.05, 0.02, 'St. 9',\n",
    "        verticalalignment='bottom', horizontalalignment='left',\n",
    "        transform=ax1.transAxes,\n",
    "        color='black', fontsize=16)\n",
    "\n",
    "#plt.tight_layout()\n",
    "#plt.draw()\n",
    "plt.savefig('St9_FeO2Chl', format='eps', dpi=300, bbox_inches = 'tight')\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "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>c1mS/cm</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>PRES</th>\n",
       "      <th>par</th>\n",
       "      <th>sbeox0Mm/L</th>\n",
       "      <th>flC</th>\n",
       "      <th>CStarTr0</th>\n",
       "      <th>flag</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>58.023980</td>\n",
       "      <td>27.8816</td>\n",
       "      <td>14.602</td>\n",
       "      <td>1.000000e-12</td>\n",
       "      <td>130.771</td>\n",
       "      <td>0.0365</td>\n",
       "      <td>98.9538</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.5245</td>\n",
       "      <td>-70.480167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>58.023980</td>\n",
       "      <td>27.8822</td>\n",
       "      <td>14.602</td>\n",
       "      <td>1.000000e-12</td>\n",
       "      <td>130.769</td>\n",
       "      <td>0.0362</td>\n",
       "      <td>98.9538</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.5245</td>\n",
       "      <td>-70.480167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>58.024836</td>\n",
       "      <td>27.8823</td>\n",
       "      <td>14.644</td>\n",
       "      <td>1.000000e-12</td>\n",
       "      <td>130.770</td>\n",
       "      <td>0.0359</td>\n",
       "      <td>98.9538</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.5245</td>\n",
       "      <td>-70.480167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58.024115</td>\n",
       "      <td>27.8823</td>\n",
       "      <td>14.626</td>\n",
       "      <td>1.000000e-12</td>\n",
       "      <td>130.769</td>\n",
       "      <td>0.0355</td>\n",
       "      <td>98.9538</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.5245</td>\n",
       "      <td>-70.480167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>58.023980</td>\n",
       "      <td>27.8828</td>\n",
       "      <td>14.602</td>\n",
       "      <td>1.000000e-12</td>\n",
       "      <td>130.768</td>\n",
       "      <td>0.0352</td>\n",
       "      <td>98.9538</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.5245</td>\n",
       "      <td>-70.480167</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     c1mS/cm     TEMP    PRES           par  sbeox0Mm/L     flC  CStarTr0  \\\n",
       "0  58.023980  27.8816  14.602  1.000000e-12     130.771  0.0365   98.9538   \n",
       "1  58.023980  27.8822  14.602  1.000000e-12     130.769  0.0362   98.9538   \n",
       "2  58.024836  27.8823  14.644  1.000000e-12     130.770  0.0359   98.9538   \n",
       "3  58.024115  27.8823  14.626  1.000000e-12     130.769  0.0355   98.9538   \n",
       "4  58.023980  27.8828  14.602  1.000000e-12     130.768  0.0352   98.9538   \n",
       "\n",
       "   flag  LATITUDE  LONGITUDE  \n",
       "0   0.0   37.5245 -70.480167  \n",
       "1   0.0   37.5245 -70.480167  \n",
       "2   0.0   37.5245 -70.480167  \n",
       "3   0.0   37.5245 -70.480167  \n",
       "4   0.0   37.5245 -70.480167  "
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read CNV files into DF\n",
    "ctd_st7.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dissolved Mn, particulate Mn, temp and O2\n",
    "plt.rcParams[\"font.family\"] = \"serif\"\n",
    "\n",
    "odv = pd.read_csv('metals_ODV.csv')\n",
    "\n",
    "#subset combined datasets for each station of interest\n",
    "stn1 = odv.loc[odv['Station'] == 1]\n",
    "stn2 = odv.loc[odv['Station'] == 2]\n",
    "stn3 = odv.loc[odv['Station'] == 3]\n",
    "stn5 = odv.loc[odv['Station'] == 5]\n",
    "stn6 = odv.loc[odv['Station'] == 6]\n",
    "stn7 = odv.loc[odv['Station'] == 7]\n",
    "stn8 = odv.loc[odv['Station'] == 8]\n",
    "stn9 = odv.loc[odv['Station'] == 9]\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(3,5))\n",
    "font_size = 16\n",
    "ax1 = fig.add_subplot(111) \n",
    "ax2 = ax1.twiny() #define second x-axis\n",
    "ax3 = ax1.twiny() #define second x-axis\n",
    "ax4 = ax1.twiny() #define second x-axis\n",
    "\n",
    "#data\n",
    "df = stn9\n",
    "ctd = ctd_st8 #St. 9 has no CTD\n",
    "\n",
    "y1 = df['depth'] \n",
    "y2 = ctd['PRES']\n",
    "x1 = df['pMn (pM)'] \n",
    "x2 = df['Mn (nM)'] \n",
    "x3 = ctd['sbeox0Mm/L'] \n",
    "x4 = ctd['TEMP']\n",
    "\n",
    "#main data colors\n",
    "x1_color = 'mediumslateblue' \n",
    "x2_color = 'magenta' \n",
    "x3_color = 'midnightblue'\n",
    "x4_color = 'limegreen'\n",
    "\n",
    "#LOD for dissolved Mn\n",
    "cond = x2 < 0.025\n",
    "\n",
    "# plot\n",
    "fig1, = ax1.plot( x1, y1, 'o--', color=x1_color, linewidth=2, markersize=8, data=df, label='pMn (pM)')\n",
    "fig2, = ax2.plot( x2, y1, 'o-', color=x2_color, linewidth=2, markersize=8, data=df, label='dMn (nM)')\n",
    "fig2, = ax2.plot( x2[cond], y1[cond], 'o', color='#D3D3D3', linewidth=2, markersize=8, data=df, label='dFe (nM)')\n",
    "#fig3, = ax3.plot( x3, y2, 'o--', color=x3_color, linewidth=2, markersize=0, data=ctd, label='O$_{2}$ ($ \\mu$M)')\n",
    "#fig4, = ax4.plot( x4, y2, 'o--', color=x4_color, linewidth=2, markersize=0, data=ctd, label='Temperaure ($\\degree$C)')\n",
    "\n",
    "\n",
    "#alter y axis\n",
    "ax1.set_ylim([0,1500])\n",
    "ax2.set_ylim([0,1500])\n",
    "ax3.set_ylim([0,1500])\n",
    "ax4.set_ylim([0,1500])\n",
    "ax1.invert_yaxis()\n",
    "\n",
    "#alter y axis\n",
    "ax1.set_xlim([0,100])\n",
    "ax2.set_xlim([0,6])\n",
    "ax3.set_xlim([0,300])\n",
    "ax4.set_xlim([0,30])\n",
    "\n",
    "#axes labels\n",
    "ax1.set_ylabel('Depth (m)', fontsize=font_size)\n",
    "ax1.set_xlabel('pMn (pM)', color=x1_color, fontsize=font_size)\n",
    "ax2.set_xlabel('dMn (nM)', color=x2_color, fontsize=font_size)\n",
    "ax3.set_xlabel('O$_{2}$ ($ \\mu$M)', color=x3_color, fontsize=font_size)\n",
    "ax4.set_xlabel('Temperaure ($\\degree$C)', color=x4_color, fontsize=font_size)\n",
    "\n",
    "#axis location and details\n",
    "ax1.spines['bottom'].set_position(('outward', 0))\n",
    "ax2.spines['top'].set_position(('outward', 0))\n",
    "ax3.spines['top'].set_position(('outward', 50))\n",
    "ax4.spines['bottom'].set_position(('outward', 50))\n",
    "\n",
    "ax1.tick_params(axis='y', colors='black', labelsize=12)\n",
    "ax1.tick_params(axis='x', colors='black', labelsize=12)\n",
    "ax2.tick_params(axis='x', colors='black', labelsize=12)\n",
    "ax3.tick_params(axis='x', colors='black', labelsize=12)\n",
    "ax4.tick_params(axis='x', colors='black', labelsize=12)\n",
    "\n",
    "ax1.xaxis.set_ticks_position('bottom')\n",
    "ax2.xaxis.set_ticks_position('top')\n",
    "ax3.xaxis.set_ticks_position('top')\n",
    "ax4.xaxis.set_ticks_position('bottom')\n",
    "\n",
    "ax1.xaxis.set_label_position('bottom')\n",
    "ax2.xaxis.set_label_position('top')\n",
    "ax3.xaxis.set_label_position('top')\n",
    "ax4.xaxis.set_label_position('bottom')\n",
    "\n",
    "ax1.text(0.05, 0.02, 'St. 9',\n",
    "        verticalalignment='bottom', horizontalalignment='left',\n",
    "        transform=ax1.transAxes,\n",
    "        color='black', fontsize=16)\n",
    "\n",
    "#plt.tight_layout()\n",
    "#plt.draw()\n",
    "plt.savefig('St9_MnO2Temp.eps', format='eps', dpi=300, bbox_inches = 'tight')\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dissolved Zn, particulate Zn, Si\n",
    "plt.rcParams[\"font.family\"] = \"serif\"\n",
    "\n",
    "odv = pd.read_csv('metals_ODV.csv')\n",
    "\n",
    "#subset combined datasets for each station of interest\n",
    "stn1 = odv.loc[odv['Station'] == 1]\n",
    "stn2 = odv.loc[odv['Station'] == 2]\n",
    "stn3 = odv.loc[odv['Station'] == 3]\n",
    "stn5 = odv.loc[odv['Station'] == 5]\n",
    "stn6 = odv.loc[odv['Station'] == 6]\n",
    "stn7 = odv.loc[odv['Station'] == 7]\n",
    "stn8 = odv.loc[odv['Station'] == 8]\n",
    "stn9 = odv.loc[odv['Station'] == 9]\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(3,5))\n",
    "font_size = 16\n",
    "ax1 = fig.add_subplot(111) \n",
    "ax2 = ax1.twiny() #define second x-axis\n",
    "ax3 = ax1.twiny() #define second x-axis\n",
    "#ax4 = ax1.twiny() #define second x-axis\n",
    "\n",
    "#data\n",
    "df = stn9\n",
    "#ctd = ctd_st9 #St. 9 has no CTD\n",
    "\n",
    "y1 = df['depth'] \n",
    "#y2 = ctd['PRES']\n",
    "x1 = df['pZn (pM)'] \n",
    "x2 = df['Zn (nM)'] \n",
    "x3 = df['Si (uM)'] \n",
    "#x4 = ctd['TEMP']\n",
    "\n",
    "#main data colors\n",
    "x1_color = 'darkgoldenrod' \n",
    "x2_color = 'orangered' \n",
    "x3_color = 'slategrey'\n",
    "#x4_color = 'limegreen'\n",
    "\n",
    "cond = x2 < 0.95\n",
    "\n",
    "# plot\n",
    "fig1, = ax1.plot( x1, y1, 'o--', color=x1_color, linewidth=2, markersize=8, data=df, label='pZn (pM)')\n",
    "fig2, = ax2.plot( x2, y1, 'o-', color=x2_color, linewidth=2, markersize=8, data=df, label='dZn (nM)')\n",
    "fig2, = ax2.plot( x2[cond], y1[cond], 'o', color='#D3D3D3', linewidth=2, markersize=8, data=df, label='dFe (nM)')\n",
    "fig3, = ax3.plot( x3, y1, 'o--', color=x3_color, linewidth=2, markersize=8, data=df, label='SiO$_{2}$ ($ \\mu$M)')\n",
    "#fig4, = ax4.plot( x4, y2, 'o--', color=x4_color, linewidth=1, markersize=0, data=ctd, label='Temperaure ($\\degree$C)')\n",
    "\n",
    "\n",
    "#alter y axis\n",
    "ax1.set_ylim([0,1500])\n",
    "ax2.set_ylim([0,1500])\n",
    "ax3.set_ylim([0,1500])\n",
    "#ax4.set_ylim([0,1500])\n",
    "ax1.invert_yaxis()\n",
    "\n",
    "#alter y axis\n",
    "ax1.set_xlim([0,150])\n",
    "ax2.set_xlim([0,15])\n",
    "ax3.set_xlim([0,15])\n",
    "#ax4.set_xlim([0,30])\n",
    "\n",
    "#axes labels\n",
    "ax1.set_ylabel('Depth (m)', fontsize=font_size)\n",
    "ax1.set_xlabel('pZn (pM)', color=x1_color, fontsize=font_size)\n",
    "ax2.set_xlabel('dZn (nM)', color=x2_color, fontsize=font_size)\n",
    "ax3.set_xlabel('SiO$_{2}$ ($ \\mu$M)', color=x3_color, fontsize=font_size)\n",
    "#ax4.set_xlabel('Temperaure ($\\degree$C)', color=x4_color, fontsize=font_size)\n",
    "\n",
    "#axis location and details\n",
    "ax1.spines['bottom'].set_position(('outward', 0))\n",
    "ax2.spines['top'].set_position(('outward', 0))\n",
    "ax3.spines['top'].set_position(('outward', 50))\n",
    "#ax4.spines['bottom'].set_position(('outward', 50))\n",
    "\n",
    "ax1.tick_params(axis='y', colors='black', labelsize=12)\n",
    "ax1.tick_params(axis='x', colors='black', labelsize=12)\n",
    "ax2.tick_params(axis='x', colors='black', labelsize=12)\n",
    "ax3.tick_params(axis='x', colors='black', labelsize=12)\n",
    "#ax4.tick_params(axis='x', colors='black', labelsize=10)\n",
    "\n",
    "ax1.xaxis.set_ticks_position('bottom')\n",
    "ax2.xaxis.set_ticks_position('top')\n",
    "ax3.xaxis.set_ticks_position('top')\n",
    "#ax4.xaxis.set_ticks_position('bottom')\n",
    "\n",
    "ax1.xaxis.set_label_position('bottom')\n",
    "ax2.xaxis.set_label_position('top')\n",
    "ax3.xaxis.set_label_position('top')\n",
    "#ax4.xaxis.set_label_position('bottom')\n",
    "\n",
    "ax1.text(0.05, 0.02, 'St. 9',\n",
    "        verticalalignment='bottom', horizontalalignment='left',\n",
    "        transform=ax1.transAxes,\n",
    "        color='black', fontsize=16)\n",
    "\n",
    "#plt.tight_layout()\n",
    "#plt.draw()\n",
    "plt.savefig('St9_ZnSi.eps', format='eps', dpi=300, bbox_inches = 'tight')\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dissolved Cd, particulate Cd, P\n",
    "plt.rcParams[\"font.family\"] = \"serif\"\n",
    "\n",
    "odv = pd.read_csv('metals_ODV.csv')\n",
    "\n",
    "#subset combined datasets for each station of interest\n",
    "stn1 = odv.loc[odv['Station'] == 1]\n",
    "stn2 = odv.loc[odv['Station'] == 2]\n",
    "stn3 = odv.loc[odv['Station'] == 3]\n",
    "stn5 = odv.loc[odv['Station'] == 5]\n",
    "stn6 = odv.loc[odv['Station'] == 6]\n",
    "stn7 = odv.loc[odv['Station'] == 7]\n",
    "stn8 = odv.loc[odv['Station'] == 8]\n",
    "stn9 = odv.loc[odv['Station'] == 9]\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(3,5))\n",
    "font_size = 16\n",
    "ax1 = fig.add_subplot(111) \n",
    "ax2 = ax1.twiny() #define second x-axis\n",
    "ax3 = ax1.twiny() #define second x-axis\n",
    "#ax4 = ax1.twiny() #define second x-axis\n",
    "\n",
    "#data\n",
    "df = stn1\n",
    "#ctd = ctd_st9 #St. 9 has no CTD\n",
    "\n",
    "y1 = df['depth'] \n",
    "#y2 = ctd['PRES']\n",
    "x1 = df['pCd (pM)'] \n",
    "x2 = df['Cd (nM)'] \n",
    "x3 = df['PO4 (uM)'] \n",
    "#x4 = ctd['TEMP']\n",
    "\n",
    "#main data colors\n",
    "x1_color = 'mediumseagreen' \n",
    "x2_color = 'darkgreen' \n",
    "x3_color = 'darkviolet'\n",
    "#x4_color = 'limegreen'\n",
    "\n",
    "cond = x2 < 0.02\n",
    "\n",
    "# plot\n",
    "fig1, = ax1.plot( x1, y1, 'o--', color=x1_color, linewidth=2, markersize=8, data=df, label='pCd (pM)')\n",
    "fig2, = ax2.plot( x2, y1, 'o-', color=x2_color, linewidth=2, markersize=8, data=df, label='dCd (nM)')\n",
    "fig2, = ax2.plot( x2[cond], y1[cond], 'o', color='#D3D3D3', linewidth=2, markersize=8, data=df, label='dFe (nM)')\n",
    "fig3, = ax3.plot( x3, y1, 'o--', color=x3_color, linewidth=2, markersize=8, data=df, label='PO$_{4}$ ($ \\mu$M)')\n",
    "#fig4, = ax4.plot( x4, y2, 'o--', color=x4_color, linewidth=1, markersize=0, data=ctd, label='Temperaure ($\\degree$C)')\n",
    "\n",
    "\n",
    "#alter y axis\n",
    "ax1.set_ylim([0,1500])\n",
    "ax2.set_ylim([0,1500])\n",
    "ax3.set_ylim([0,1500])\n",
    "#ax4.set_ylim([0,1500])\n",
    "ax1.invert_yaxis()\n",
    "\n",
    "#alter y axis\n",
    "ax1.set_xlim([0,3.2])\n",
    "ax2.set_xlim([0,0.5])\n",
    "ax3.set_xlim([0,2])\n",
    "#ax4.set_xlim([0,30])\n",
    "\n",
    "#axes labels\n",
    "ax1.set_ylabel('Depth (m)', fontsize=font_size)\n",
    "ax1.set_xlabel('pCd (pM)', color=x1_color, fontsize=font_size)\n",
    "ax2.set_xlabel('dCd (nM)', color=x2_color, fontsize=font_size)\n",
    "ax3.set_xlabel('PO$_{4}$ ($ \\mu$M)', color=x3_color, fontsize=font_size)\n",
    "#ax4.set_xlabel('Temperaure ($\\degree$C)', color=x4_color, fontsize=font_size)\n",
    "\n",
    "#axis location and details\n",
    "ax1.spines['bottom'].set_position(('outward', 0))\n",
    "ax2.spines['top'].set_position(('outward', 0))\n",
    "ax3.spines['top'].set_position(('outward', 50))\n",
    "#ax4.spines['bottom'].set_position(('outward', 50))\n",
    "\n",
    "ax1.tick_params(axis='y', colors='black', labelsize=12)\n",
    "ax1.tick_params(axis='x', colors='black', labelsize=12)\n",
    "ax2.tick_params(axis='x', colors='black', labelsize=12)\n",
    "ax3.tick_params(axis='x', colors='black', labelsize=12)\n",
    "#ax4.tick_params(axis='x', colors='black', labelsize=10)\n",
    "\n",
    "ax1.xaxis.set_ticks_position('bottom')\n",
    "ax2.xaxis.set_ticks_position('top')\n",
    "ax3.xaxis.set_ticks_position('top')\n",
    "#ax4.xaxis.set_ticks_position('bottom')\n",
    "\n",
    "ax1.xaxis.set_label_position('bottom')\n",
    "ax2.xaxis.set_label_position('top')\n",
    "ax3.xaxis.set_label_position('top')\n",
    "#ax4.xaxis.set_label_position('bottom')\n",
    "\n",
    "ax1.text(0.05, 0.02, 'St. 1',\n",
    "        verticalalignment='bottom', horizontalalignment='left',\n",
    "        transform=ax1.transAxes,\n",
    "        color='black', fontsize=16)\n",
    "\n",
    "#plt.tight_layout()\n",
    "#plt.draw()\n",
    "plt.savefig('St1_CdP.eps', format='eps', dpi=300, bbox_inches = 'tight')\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dissolved Ni, particulate Ni, NO3\n",
    "plt.rcParams[\"font.family\"] = \"serif\"\n",
    "\n",
    "odv = pd.read_csv('metals_ODV.csv')\n",
    "\n",
    "#subset combined datasets for each station of interest\n",
    "stn1 = odv.loc[odv['Station'] == 1]\n",
    "stn2 = odv.loc[odv['Station'] == 2]\n",
    "stn3 = odv.loc[odv['Station'] == 3]\n",
    "stn5 = odv.loc[odv['Station'] == 5]\n",
    "stn6 = odv.loc[odv['Station'] == 6]\n",
    "stn7 = odv.loc[odv['Station'] == 7]\n",
    "stn8 = odv.loc[odv['Station'] == 8]\n",
    "stn9 = odv.loc[odv['Station'] == 9]\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(3,5))\n",
    "font_size = 16\n",
    "ax1 = fig.add_subplot(111) \n",
    "ax2 = ax1.twiny() #define second x-axis\n",
    "ax3 = ax1.twiny() #define second x-axis\n",
    "#ax4 = ax1.twiny() #define second x-axis\n",
    "\n",
    "#data\n",
    "df = stn9\n",
    "#ctd = ctd_st9 #St. 9 has no CTD\n",
    "\n",
    "y1 = df['depth'] \n",
    "#y2 = ctd['PRES']\n",
    "x1 = df['pNi (pM)'] \n",
    "x2 = df['Ni (nM)'] \n",
    "x3 = df['NO2+NO3 (uM)'] \n",
    "#x4 = ctd['TEMP']\n",
    "\n",
    "#main data colors\n",
    "x1_color = 'darkseagreen' \n",
    "x2_color = 'olive' \n",
    "x3_color = 'crimson'\n",
    "#x4_color = 'limegreen'\n",
    "\n",
    "cond = x2 < 0.07\n",
    "\n",
    "# plot\n",
    "fig1, = ax1.plot( x1, y1, 'o--', color=x1_color, linewidth=2, markersize=8, data=df, label='pNi (pM)')\n",
    "fig2, = ax2.plot( x2, y1, 'o-', color=x2_color, linewidth=2, markersize=8, data=df, label='dNi (nM)')\n",
    "fig2, = ax2.plot( x2[cond], y1[cond], 'o', color='#D3D3D3', linewidth=2, markersize=8, data=df, label='dFe (nM)')\n",
    "fig3, = ax3.plot( x3, y1, 'o--', color=x3_color, linewidth=2, markersize=8, data=df, label='NO$_{3}$ + NO$_{2}$ ($ \\mu$M)')\n",
    "#fig4, = ax4.plot( x4, y2, 'o--', color=x4_color, linewidth=1, markersize=0, data=ctd, label='Temperaure ($\\degree$C)')\n",
    "\n",
    "\n",
    "#alter y axis\n",
    "ax1.set_ylim([0,1500])\n",
    "ax2.set_ylim([0,1500])\n",
    "ax3.set_ylim([0,1500])\n",
    "#ax4.set_ylim([0,1500])\n",
    "ax1.invert_yaxis()\n",
    "\n",
    "#alter y axis\n",
    "ax1.set_xlim([0,15])\n",
    "ax2.set_xlim([0,5])\n",
    "ax3.set_xlim([0,25])\n",
    "#ax4.set_xlim([0,30])\n",
    "\n",
    "#axes labels\n",
    "ax1.set_ylabel('Depth (m)', fontsize=font_size)\n",
    "ax1.set_xlabel('pNi (pM)', color=x1_color, fontsize=font_size)\n",
    "ax2.set_xlabel('dNi (nM)', color=x2_color, fontsize=font_size)\n",
    "ax3.set_xlabel('NO$_{3}$ + NO$_{2}$ ($ \\mu$M)', color=x3_color, fontsize=font_size)\n",
    "#ax4.set_xlabel('Temperaure ($\\degree$C)', color=x4_color, fontsize=font_size)\n",
    "\n",
    "#axis location and details\n",
    "ax1.spines['bottom'].set_position(('outward', 0))\n",
    "ax2.spines['top'].set_position(('outward', 0))\n",
    "ax3.spines['top'].set_position(('outward', 50))\n",
    "#ax4.spines['bottom'].set_position(('outward', 50))\n",
    "\n",
    "ax1.tick_params(axis='y', colors='black', labelsize=12)\n",
    "ax1.tick_params(axis='x', colors='black', labelsize=12)\n",
    "ax2.tick_params(axis='x', colors='black', labelsize=12)\n",
    "ax3.tick_params(axis='x', colors='black', labelsize=12)\n",
    "#ax4.tick_params(axis='x', colors='black', labelsize=10)\n",
    "\n",
    "ax1.xaxis.set_ticks_position('bottom')\n",
    "ax2.xaxis.set_ticks_position('top')\n",
    "ax3.xaxis.set_ticks_position('top')\n",
    "#ax4.xaxis.set_ticks_position('bottom')\n",
    "\n",
    "ax1.xaxis.set_label_position('bottom')\n",
    "ax2.xaxis.set_label_position('top')\n",
    "ax3.xaxis.set_label_position('top')\n",
    "#ax4.xaxis.set_label_position('bottom')\n",
    "\n",
    "ax1.text(0.05, 0.02, 'St. 9',\n",
    "        verticalalignment='bottom', horizontalalignment='left',\n",
    "        transform=ax1.transAxes,\n",
    "        color='black', fontsize=16)\n",
    "\n",
    "#plt.tight_layout()\n",
    "#plt.draw()\n",
    "plt.savefig('St9_NiNO3.eps', format='eps', dpi=300, bbox_inches = 'tight')\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dissolved Cu, particulate Cu, NO2\n",
    "plt.rcParams[\"font.family\"] = \"serif\"\n",
    "\n",
    "odv = pd.read_csv('metals_ODV.csv')\n",
    "\n",
    "#subset combined datasets for each station of interest\n",
    "stn1 = odv.loc[odv['Station'] == 1]\n",
    "stn2 = odv.loc[odv['Station'] == 2]\n",
    "stn3 = odv.loc[odv['Station'] == 3]\n",
    "stn5 = odv.loc[odv['Station'] == 5]\n",
    "stn6 = odv.loc[odv['Station'] == 6]\n",
    "stn7 = odv.loc[odv['Station'] == 7]\n",
    "stn8 = odv.loc[odv['Station'] == 8]\n",
    "stn9 = odv.loc[odv['Station'] == 9]\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(3,5))\n",
    "font_size = 16\n",
    "ax1 = fig.add_subplot(111) \n",
    "ax2 = ax1.twiny() #define second x-axis\n",
    "ax3 = ax1.twiny() #define second x-axis\n",
    "#ax4 = ax1.twiny() #define second x-axis\n",
    "\n",
    "#data\n",
    "df = stn9\n",
    "#ctd = ctd_st9 #St. 9 has no CTD\n",
    "\n",
    "y1 = df['depth'] \n",
    "#y2 = ctd['PRES']\n",
    "x1 = df['pCu (pM)'] \n",
    "x2 = df['Cu (nM)'] \n",
    "x3 = df['NO2 (uM)'] \n",
    "#x4 = ctd['TEMP']\n",
    "\n",
    "#main data colors\n",
    "x1_color = 'deepskyblue' \n",
    "x2_color = 'navy' \n",
    "x3_color = 'palevioletred'\n",
    "#x4_color = 'limegreen'\n",
    "\n",
    "cond = x2 < 0.12\n",
    "\n",
    "# plot\n",
    "fig1, = ax1.plot( x1, y1, 'o--', color=x1_color, linewidth=2, markersize=8, data=df, label='pCu (pM)')\n",
    "fig2, = ax2.plot( x2, y1, 'o-', color=x2_color, linewidth=2, markersize=8, data=df, label='dCu (nM)')\n",
    "fig2, = ax2.plot( x2[cond], y1[cond], 'o', color='#D3D3D3', linewidth=2, markersize=8, data=df, label='dFe (nM)')\n",
    "fig3, = ax3.plot( x3, y1, 'o--', color=x3_color, linewidth=2, markersize=8, data=df, label='NO$_{2}$ ($ \\mu$M)')\n",
    "#fig4, = ax4.plot( x4, y2, 'o--', color=x4_color, linewidth=1, markersize=0, data=ctd, label='Temperaure ($\\degree$C)')\n",
    "\n",
    "\n",
    "#alter y axis\n",
    "ax1.set_ylim([0,1500])\n",
    "ax2.set_ylim([0,1500])\n",
    "ax3.set_ylim([0,1500])\n",
    "#ax4.set_ylim([0,1500])\n",
    "ax1.invert_yaxis()\n",
    "\n",
    "#alter y axis\n",
    "ax1.set_xlim([0,80])\n",
    "ax2.set_xlim([0,2])\n",
    "ax3.set_xlim([0,0.2])\n",
    "#ax4.set_xlim([0,30])\n",
    "\n",
    "#axes labels\n",
    "ax1.set_ylabel('Depth (m)', fontsize=font_size)\n",
    "ax1.set_xlabel('pCu (pM)', color=x1_color, fontsize=font_size)\n",
    "ax2.set_xlabel('dCu (nM)', color=x2_color, fontsize=font_size)\n",
    "ax3.set_xlabel('NO$_{2}$ ($ \\mu$M)', color=x3_color, fontsize=font_size)\n",
    "#ax4.set_xlabel('Temperaure ($\\degree$C)', color=x4_color, fontsize=font_size)\n",
    "\n",
    "#axis location and details\n",
    "ax1.spines['bottom'].set_position(('outward', 0))\n",
    "ax2.spines['top'].set_position(('outward', 0))\n",
    "ax3.spines['top'].set_position(('outward', 50))\n",
    "#ax4.spines['bottom'].set_position(('outward', 50))\n",
    "\n",
    "ax1.tick_params(axis='y', colors='black', labelsize=12)\n",
    "ax1.tick_params(axis='x', colors='black', labelsize=12)\n",
    "ax2.tick_params(axis='x', colors='black', labelsize=12)\n",
    "ax3.tick_params(axis='x', colors='black', labelsize=12)\n",
    "#ax4.tick_params(axis='x', colors='black', labelsize=10)\n",
    "\n",
    "ax1.xaxis.set_ticks_position('bottom')\n",
    "ax2.xaxis.set_ticks_position('top')\n",
    "ax3.xaxis.set_ticks_position('top')\n",
    "#ax4.xaxis.set_ticks_position('bottom')\n",
    "\n",
    "ax1.xaxis.set_label_position('bottom')\n",
    "ax2.xaxis.set_label_position('top')\n",
    "ax3.xaxis.set_label_position('top')\n",
    "#ax4.xaxis.set_label_position('bottom')\n",
    "\n",
    "ax1.text(0.05, 0.02, 'St. 9',\n",
    "        verticalalignment='bottom', horizontalalignment='left',\n",
    "        transform=ax1.transAxes,\n",
    "        color='black', fontsize=16)\n",
    "\n",
    "#plt.tight_layout()\n",
    "#plt.draw()\n",
    "plt.savefig('St9_CuNO2.eps', format='eps', dpi=300, bbox_inches = 'tight')\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Station</th>\n",
       "      <th>depth</th>\n",
       "      <th>Fe</th>\n",
       "      <th>Zn</th>\n",
       "      <th>Cd</th>\n",
       "      <th>Ni</th>\n",
       "      <th>Cu</th>\n",
       "      <th>Mn</th>\n",
       "      <th>...</th>\n",
       "      <th>NO2+NO3</th>\n",
       "      <th>Si</th>\n",
       "      <th>NO2</th>\n",
       "      <th>NH4</th>\n",
       "      <th>pMn</th>\n",
       "      <th>pFe</th>\n",
       "      <th>pNi</th>\n",
       "      <th>pCu</th>\n",
       "      <th>pZn</th>\n",
       "      <th>pCd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31.666888</td>\n",
       "      <td>-64.166293</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>0.535234</td>\n",
       "      <td>1.013424</td>\n",
       "      <td>0.001253</td>\n",
       "      <td>2.004302</td>\n",
       "      <td>0.854536</td>\n",
       "      <td>2.395847</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.19</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>31.666888</td>\n",
       "      <td>-64.166293</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>0.531819</td>\n",
       "      <td>0.671165</td>\n",
       "      <td>0.003520</td>\n",
       "      <td>1.958119</td>\n",
       "      <td>0.888045</td>\n",
       "      <td>2.544319</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.09</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>2</th>\n",
       "      <td>31.666888</td>\n",
       "      <td>-64.166293</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>0.736866</td>\n",
       "      <td>0.748252</td>\n",
       "      <td>0.003182</td>\n",
       "      <td>1.972479</td>\n",
       "      <td>0.996230</td>\n",
       "      <td>2.158336</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.15</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>3</th>\n",
       "      <td>31.666888</td>\n",
       "      <td>-64.166293</td>\n",
       "      <td>1</td>\n",
       "      <td>115</td>\n",
       "      <td>0.185665</td>\n",
       "      <td>0.971132</td>\n",
       "      <td>0.007250</td>\n",
       "      <td>2.003143</td>\n",
       "      <td>0.908902</td>\n",
       "      <td>1.298053</td>\n",
       "      <td>...</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31.666888</td>\n",
       "      <td>-64.166293</td>\n",
       "      <td>1</td>\n",
       "      <td>150</td>\n",
       "      <td>0.237065</td>\n",
       "      <td>1.444574</td>\n",
       "      <td>0.006938</td>\n",
       "      <td>2.008332</td>\n",
       "      <td>0.928732</td>\n",
       "      <td>1.185590</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.16</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Latitude  Longitude  Station  depth        Fe        Zn        Cd  \\\n",
       "0  31.666888 -64.166293        1     10  0.535234  1.013424  0.001253   \n",
       "1  31.666888 -64.166293        1     30  0.531819  0.671165  0.003520   \n",
       "2  31.666888 -64.166293        1     60  0.736866  0.748252  0.003182   \n",
       "3  31.666888 -64.166293        1    115  0.185665  0.971132  0.007250   \n",
       "4  31.666888 -64.166293        1    150  0.237065  1.444574  0.006938   \n",
       "\n",
       "         Ni        Cu        Mn  ...  NO2+NO3   Si   NO2   NH4  pMn  pFe  pNi  \\\n",
       "0  2.004302  0.854536  2.395847  ...      0.3  0.1  0.04  0.19  NaN  NaN  NaN   \n",
       "1  1.958119  0.888045  2.544319  ...      0.2  0.3  0.02  0.09  NaN  NaN  NaN   \n",
       "2  1.972479  0.996230  2.158336  ...      0.1  0.1  0.03  0.15  NaN  NaN  NaN   \n",
       "3  2.003143  0.908902  1.298053  ...      1.4  0.4  0.10  0.00  NaN  NaN  NaN   \n",
       "4  2.008332  0.928732  1.185590  ...      1.0  0.1  0.03  0.16  NaN  NaN  NaN   \n",
       "\n",
       "   pCu  pZn  pCd  \n",
       "0  NaN  NaN  NaN  \n",
       "1  NaN  NaN  NaN  \n",
       "2  NaN  NaN  NaN  \n",
       "3  NaN  NaN  NaN  \n",
       "4  NaN  NaN  NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "odv = pd.read_csv('metals_ODV.csv')\n",
    "plt.rcParams[\"font.family\"] = \"serif\"\n",
    "odv.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = np.polyfit(odv['PO4 (uM)'],odv['Cd (nM)'],1)\n",
    "f = np.poly1d(d)\n",
    "odv.insert(5,'Cd:P slope',f(odv['PO4']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax=odv.plot.scatter(x='PO4', y='Cd', color='black')\n",
    "odv.plot(x='PO4',y='Cd:P slope',color='Red',legend=False,ax=ax, xlabel=('NO$_{2}$ ($ \\mu$M)'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Salinity correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "ctd = pd.read_csv('Salinity_metals.csv')\n",
    "# Salinity from BATS CTD - no data for St. 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Add axis labels, connect the dots, invert the plot\n",
    "fig = plt.figure(figsize=(4,4))\n",
    "ax1 = fig.add_subplot(111)\n",
    "\n",
    "y = ctd['Cu (nM)'] \n",
    "x = ctd['Salinity (PSU)']\n",
    "\n",
    "#Change look of plot\n",
    "x1_color = 'navy' \n",
    "font_size = 12\n",
    "\n",
    "# plot\n",
    "fig1 = ax1.plot(x, y, 'o', color=x1_color, linewidth=2, markersize=5, data=ctd)\n",
    "\n",
    "\n",
    "#axes labels\n",
    "#ax1.set_xlabel('Depth (m)', fontsize=font_size)\n",
    "ax1.set_xlabel('Salinity (PSU)', color=x1_color, fontsize=font_size)\n",
    "ax1.set_ylabel('Cu (nM)', color=x1_color, fontsize=font_size)\n",
    "\n",
    "\n",
    "import scipy.stats\n",
    "m, b, r_value, p_value, std_err = scipy.stats.linregress(x, y)\n",
    "ax1.plot(x, m*x+b, '-', color='black', linewidth=0.5)\n",
    "ax1.annotate('r$^2$ = ' + str(\"{:.2f}\".format(r_value**2)), xy=(35.5,1.8))\n",
    "ax1.annotate('y = ' + str(\"{:.2f}\".format(m)) + 'x + ' + str(\"{:.2f}\".format(b)), xy=(35.5,1.72))\n",
    "fig.savefig('scatter_Cu.eps', format='eps', bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Add axis labels, connect the dots, invert the plot\n",
    "fig = plt.figure(figsize=(4,4))\n",
    "ax1 = fig.add_subplot(111)\n",
    "\n",
    "y = ctd['Zn (nM)'] \n",
    "x = ctd['Salinity (PSU)']\n",
    "\n",
    "#Change look of plot\n",
    "x1_color = 'orangered' \n",
    "font_size = 12\n",
    "\n",
    "# plot\n",
    "fig1 = ax1.plot(x, y, 'o', color=x1_color, linewidth=2, markersize=5, data=ctd)\n",
    "\n",
    "\n",
    "#axes labels\n",
    "#ax1.set_xlabel('Depth (m)', fontsize=font_size)\n",
    "ax1.set_xlabel('Salinity (PSU)', color=x1_color, fontsize=font_size)\n",
    "ax1.set_ylabel('Zn (nM)', color=x1_color, fontsize=font_size)\n",
    "\n",
    "\n",
    "import scipy.stats\n",
    "m, b, r_value, p_value, std_err = scipy.stats.linregress(x, y)\n",
    "ax1.plot(x, m*x+b, color='black',linewidth=0.5)\n",
    "ax1.annotate('r$^2$ = ' + str(\"{:.2f}\".format(r_value**2)), xy=(35.25,7.5))\n",
    "ax1.annotate('y = ' + str(\"{:.2f}\".format(m)) + 'x + ' + str(\"{:.2f}\".format(b)), xy=(35.25,7))\n",
    "fig.savefig('scatter_Zn.eps', format='eps', bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Add axis labels, connect the dots, invert the plot\n",
    "fig = plt.figure(figsize=(4,4))\n",
    "ax1 = fig.add_subplot(111)\n",
    "\n",
    "y = ctd['Ni (nM)'] \n",
    "x = ctd['Salinity (PSU)']\n",
    "\n",
    "#Change look of plot\n",
    "x1_color = 'olive' \n",
    "font_size = 12\n",
    "\n",
    "# plot\n",
    "fig1 = ax1.plot(x, y, 'o', color=x1_color, linewidth=2, markersize=5, data=ctd)\n",
    "\n",
    "\n",
    "#axes labels\n",
    "#ax1.set_xlabel('Depth (m)', fontsize=font_size)\n",
    "ax1.set_xlabel('Salinity (PSU)', color=x1_color, fontsize=font_size)\n",
    "ax1.set_ylabel('Ni (nM)', color=x1_color, fontsize=font_size)\n",
    "\n",
    "\n",
    "import scipy.stats\n",
    "m, b, r_value, p_value, std_err = scipy.stats.linregress(x, y)\n",
    "ax1.plot(x, m*x+b,color='black',linewidth=0.5)\n",
    "ax1.annotate('r$^2$ = ' + str(\"{:.2f}\".format(r_value**2)), xy=(36.25,2.9))\n",
    "ax1.annotate('y = ' + str(\"{:.2f}\".format(m)) + 'x + ' + str(\"{:.2f}\".format(b)), xy=(36.15,2.83))\n",
    "fig.savefig('scatter_Ni.eps', format='eps', bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Add axis labels, connect the dots, invert the plot\n",
    "fig = plt.figure(figsize=(4,4))\n",
    "ax1 = fig.add_subplot(111)\n",
    "\n",
    "y = ctd['Cd (nM)'] \n",
    "x = ctd['Salinity (PSU)']\n",
    "\n",
    "#Change look of plot\n",
    "x1_color = 'darkgreen' \n",
    "font_size = 12\n",
    "\n",
    "# plot\n",
    "fig1 = ax1.plot(x, y, 'o', color=x1_color, linewidth=2, markersize=5, data=ctd)\n",
    "\n",
    "\n",
    "#axes labels\n",
    "#ax1.set_xlabel('Depth (m)', fontsize=font_size)\n",
    "ax1.set_xlabel('Salinity (PSU)', color=x1_color, fontsize=font_size)\n",
    "ax1.set_ylabel('Cd (nM)', color=x1_color, fontsize=font_size)\n",
    "\n",
    "\n",
    "import scipy.stats\n",
    "m, b, r_value, p_value, std_err = scipy.stats.linregress(x, y)\n",
    "ax1.plot(x, m*x+b,color='black',linewidth=0.5)\n",
    "ax1.annotate('r$^2$ = ' + str(\"{:.2f}\".format(r_value**2)), xy=(36.25,0.165))\n",
    "ax1.annotate('y = ' + str(\"{:.2f}\".format(m)) + 'x + ' + str(\"{:.2f}\".format(b)), xy=(36.18,0.155))\n",
    "fig.savefig('scatter_Cd.eps', format='eps', bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Add axis labels, connect the dots, invert the plot\n",
    "fig = plt.figure(figsize=(4,4))\n",
    "ax1 = fig.add_subplot(111)\n",
    "\n",
    "# ! WARNING = removes 2 rows with \"NA\" for Mn concentrations\n",
    "ctd = ctd.drop(ctd.index[6], axis=0)\n",
    "ctd = ctd.drop(ctd.index[25], axis=0)\n",
    "\n",
    "y = ctd['Mn (nM)'] \n",
    "x = ctd['Salinity (PSU)']\n",
    "\n",
    "#Change look of plot\n",
    "x1_color = 'magenta' \n",
    "font_size = 12\n",
    "\n",
    "# plot\n",
    "fig1 = ax1.plot(x, y, 'o', color=x1_color, linewidth=2, markersize=5, data=ctd)\n",
    "\n",
    "\n",
    "#axes labels\n",
    "#ax1.set_xlabel('Depth (m)', fontsize=font_size)\n",
    "ax1.set_xlabel('Salinity (PSU)', color=x1_color, fontsize=font_size)\n",
    "ax1.set_ylabel('Mn (nM)', color=x1_color, fontsize=font_size)\n",
    "\n",
    "import scipy.stats\n",
    "m, b, r_value, p_value, std_err = scipy.stats.linregress(x, y)\n",
    "ax1.plot(x, m*x+b,color='black',linewidth=0.5)\n",
    "ax1.annotate('r$^2$ = ' + str(\"{:.2f}\".format(r_value**2)), xy=(35.25,4))\n",
    "ax1.annotate('y = ' + str(\"{:.2f}\".format(m)) + 'x + ' + str(\"{:.2f}\".format(b)), xy=(35.25,3.7))\n",
    "fig.savefig('scatter_Mn.eps', format='eps', bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pigment, protein, transcript concentration correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.read_csv('pigments_trans_prot.csv')\n",
    "\n",
    "#Proteins vs pigments\n",
    "subset = data[[\"Proteins( spectral counts/L)\", \"Pigments(ng/kg)\"]]\n",
    "fix = subset.dropna()\n",
    "\n",
    "#Add axis labels, connect the dots, invert the plot\n",
    "fig = plt.figure(figsize=(4,4))\n",
    "ax1 = fig.add_subplot(111)\n",
    "\n",
    "y = np.log2(fix['Proteins( spectral counts/L)'])\n",
    "x = np.log2(fix['Pigments(ng/kg)']+1)\n",
    "\n",
    "#Change look of plot\n",
    "x1_color = 'black' \n",
    "font_size = 12\n",
    "\n",
    "# plot\n",
    "fig1 = ax1.plot(x, y, 'o', color=x1_color, linewidth=2, markersize=5, data=fix)\n",
    "\n",
    "#axes labels\n",
    "#ax1.set_xlabel('Depth (m)', fontsize=font_size)\n",
    "ax1.set_xlabel('log$_{2}$ pigments (ng kg$^{-1}$)', color=x1_color, fontsize=font_size)\n",
    "ax1.set_ylabel('log$_{2}$ proteins (spectral counts L$^{-1}$)', color=x1_color, fontsize=font_size)\n",
    "\n",
    "import scipy.stats\n",
    "m, b, r_value, p_value, std_err = scipy.stats.linregress(x, y)\n",
    "ax1.plot(x, m*x+b, '-', color='black', linewidth=0.5)\n",
    "ax1.annotate('r$^2$ = ' + str(\"{:.2f}\".format(r_value**2)), xy=(1,15))\n",
    "ax1.annotate('y = ' + str(\"{:.4f}\".format(m)) + 'x + ' + str(\"{:.4f}\".format(b)), xy=(0.5,15.5))\n",
    "fig.savefig('prot_pigment.eps', format='eps', bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Proteins vs transcripts\n",
    "data = pd.read_csv('pigments_trans_prot.csv')\n",
    "data = data[data['ID'] != '7_500']\n",
    "subset = data[[\"Proteins( spectral counts/L)\", \"Transcripts(copiesL)\"]]\n",
    "fix = subset.dropna()\n",
    "\n",
    "#Add axis labels, connect the dots, invert the plot\n",
    "fig = plt.figure(figsize=(4,4))\n",
    "ax1 = fig.add_subplot(111)\n",
    "\n",
    "y = np.log2(fix['Transcripts(copiesL)'])\n",
    "x = np.log2(fix['Proteins( spectral counts/L)'])\n",
    "\n",
    "#Change look of plot\n",
    "x1_color = 'black' \n",
    "font_size = 12\n",
    "\n",
    "# plot\n",
    "fig1 = ax1.plot(x, y, 'o', color=x1_color, linewidth=2, markersize=5, data=fix)\n",
    "\n",
    "#axes labels\n",
    "#ax1.set_xlabel('Depth (m)', fontsize=font_size)\n",
    "ax1.set_xlabel('log$_{2}$ proteins (spectral counts L$^{-1}$)', color=x1_color, fontsize=font_size)\n",
    "ax1.set_ylabel('log$_{2}$ transcripts (copies L$^{-1}$)', color=x1_color, fontsize=font_size)\n",
    "\n",
    "import scipy.stats\n",
    "m, b, r_value, p_value, std_err = scipy.stats.linregress(x, y)\n",
    "ax1.plot(x, m*x+b, '-', color='black', linewidth=0.5)\n",
    "ax1.annotate('r$^2$ = ' + str(\"{:.2f}\".format(r_value**2)), xy=(9.5,16))\n",
    "ax1.annotate('y = ' + str(\"{:.2f}\".format(m)) + 'x + ' + str(\"{:.0f}\".format(b)), xy=(9.5,18))\n",
    "fig.savefig('prot_trans.eps', format='eps', bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Transcripts vs pigments\n",
    "data = pd.read_csv('pigments_trans_prot.csv')\n",
    "subset = data[[\"Pigments(ng/kg)\", \"Factor\"]]\n",
    "fix = subset.dropna()\n",
    "\n",
    "#Add axis labels, connect the dots, invert the plot\n",
    "fig = plt.figure(figsize=(4,4))\n",
    "ax1 = fig.add_subplot(111)\n",
    "\n",
    "y = np.log2(fix['Factor'])\n",
    "x = np.log2(fix['Pigments(ng/kg)']+1)\n",
    "\n",
    "#Change look of plot\n",
    "x1_color = 'black' \n",
    "font_size = 12\n",
    "\n",
    "# plot\n",
    "fig1 = ax1.plot(x, y, 'o', color=x1_color, linewidth=2, markersize=5, data=fix)\n",
    "\n",
    "#axes labels\n",
    "#ax1.set_xlabel('Depth (m)', fontsize=font_size)\n",
    "ax1.set_xlabel('log$_{2}$ pigments (ng kg$^{-1}$)', color=x1_color, fontsize=font_size)\n",
    "ax1.set_ylabel('log$_{2}$ transcripts (copies L$^{-1}$)', color=x1_color, fontsize=font_size)\n",
    "\n",
    "import scipy.stats\n",
    "m, b, r_value, p_value, std_err = scipy.stats.linregress(x, y)\n",
    "ax1.plot(x, m*x+b, '-', color='black', linewidth=0.5)\n",
    "ax1.annotate('r$^2$ = ' + str(\"{:.2f}\".format(r_value**2)), xy=(0,22))\n",
    "ax1.annotate('y = ' + str(\"{:.2f}\".format(m)) + 'x + ' + str(\"{:.0f}\".format(b)), xy=(0,23))\n",
    "fig.savefig('trans_pigment.eps', format='eps', bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fungi vs beam transmission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ctd = ctd_st6\n",
    "df = pd.read_csv('fungiprofilecountsL.csv')\n",
    "df2 = pd.read_csv('fungiprofile.csv')\n",
    "\n",
    "fig = plt.figure(figsize=(10,4))\n",
    "font_size = 12\n",
    "ax1 = fig.add_subplot(131) \n",
    "\n",
    "y1 = df2['Depth'] \n",
    "x1 = df2[' Fungi (Spectral counts)'] \n",
    "\n",
    "#main data colors\n",
    "x1_color = 'hotpink' \n",
    "# plot\n",
    "fig1, = ax1.plot( x1, y1, 'o--', color=x1_color, linewidth=2, markersize=8, data=df, label='Fungi proteins (spectral counts/L)')\n",
    "#alter y axis\n",
    "ax1.set_ylim([0,4400])\n",
    "ax1.invert_yaxis()\n",
    "#alter y axis\n",
    "ax1.set_xlim([0,350])\n",
    "#axes labels\n",
    "ax1.set_ylabel('Depth (m)', fontsize=font_size)\n",
    "ax1.set_xlabel('Fungi proteins (Spectral counts)', color=x1_color, fontsize=font_size)\n",
    "\n",
    "#axis location and details\n",
    "ax1.spines['bottom'].set_position(('outward', 0))\n",
    "ax1.tick_params(axis='y', colors='black', labelsize=12)\n",
    "ax1.tick_params(axis='x', colors='black', labelsize=12)\n",
    "ax1.xaxis.set_ticks_position('bottom')\n",
    "ax1.xaxis.set_label_position('bottom')\n",
    "ax1.text(0.05, 0.02, 'St. 6',\n",
    "        verticalalignment='bottom', horizontalalignment='left',\n",
    "        transform=ax1.transAxes,\n",
    "        color='black', fontsize=12)\n",
    "\n",
    "#plt.savefig('Fungi_transmissometer.eps', format='eps', dpi=300, bbox_inches = 'tight')\n",
    "\n",
    "#Spectral counts/L)\n",
    "font_size = 12\n",
    "ax2 = fig.add_subplot(132) \n",
    "\n",
    "y1 = df['Depth'] \n",
    "x1 = df[' Fungi (Spectral counts/L)'] \n",
    "\n",
    "#main data colors\n",
    "x1_color = 'hotpink' \n",
    "# plot\n",
    "fig1, = ax2.plot( x1, y1, 'o--', color=x1_color, linewidth=2, markersize=8, data=df, label='Fungi proteins (spectral counts/L)')\n",
    "#alter y axis\n",
    "ax2.set_ylim([0,4400])\n",
    "ax2.invert_yaxis()\n",
    "#alter y axis\n",
    "ax2.set_xlim([0,32])\n",
    "#axes labels\n",
    "ax2.set_ylabel('Depth (m)', fontsize=font_size)\n",
    "ax2.set_xlabel('Fungi proteins (Spectral counts/L)', color=x1_color, fontsize=font_size)\n",
    "\n",
    "#axis location and details\n",
    "ax2.spines['bottom'].set_position(('outward', 0))\n",
    "ax2.tick_params(axis='y', colors='black', labelsize=12)\n",
    "ax2.tick_params(axis='x', colors='black', labelsize=12)\n",
    "ax2.xaxis.set_ticks_position('bottom')\n",
    "ax2.xaxis.set_label_position('bottom')\n",
    "ax2.text(0.05, 0.02, 'St. 6',\n",
    "        verticalalignment='bottom', horizontalalignment='left',\n",
    "        transform=ax1.transAxes,\n",
    "        color='black', fontsize=12)\n",
    "\n",
    "#Beam\n",
    "font_size = 12\n",
    "ax3 = fig.add_subplot(133) \n",
    "\n",
    "y1 = ctd['DEPTH'] \n",
    "x1 = ctd['CStarTr0'] \n",
    "\n",
    "#main data colors\n",
    "x1_color = 'black' \n",
    "# plot\n",
    "fig1 = ax3.plot( x1, y1, '-', color=x1_color, linewidth=1, markersize=8, data=ctd, label='Beam transmission (%)')\n",
    "#alter y axis\n",
    "ax3.set_ylim([0,4400])\n",
    "ax3.invert_yaxis()\n",
    "#alter y axis\n",
    "ax3.set_xlim([90,102])\n",
    "#axes labels\n",
    "ax3.set_ylabel('Depth (m)', fontsize=font_size)\n",
    "ax3.set_xlabel('Beam transmission (%)', color=x1_color, fontsize=font_size)\n",
    "\n",
    "#axis location and details\n",
    "ax3.spines['bottom'].set_position(('outward', 0))\n",
    "ax3.tick_params(axis='y', colors='black', labelsize=12)\n",
    "ax3.tick_params(axis='x', colors='black', labelsize=12)\n",
    "ax3.xaxis.set_ticks_position('bottom')\n",
    "ax3.xaxis.set_label_position('bottom')\n",
    "ax3.text(0.05, 0.02, 'St. 6',\n",
    "        verticalalignment='bottom', horizontalalignment='left',\n",
    "        transform=ax2.transAxes,\n",
    "        color='black', fontsize=12)\n",
    "\n",
    "#plt.tight_layout()\n",
    "plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.5, hspace=None)\n",
    "plt.savefig('Fungi_transmissometer.eps', format='eps', dpi=300, bbox_inches = 'tight')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>ID</th>\n",
       "      <th>Depth</th>\n",
       "      <th>Fungi (Spectral counts/L)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6_20</td>\n",
       "      <td>20</td>\n",
       "      <td>5.710345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6_40</td>\n",
       "      <td>40</td>\n",
       "      <td>3.112392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6_70</td>\n",
       "      <td>70</td>\n",
       "      <td>1.380000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6_110</td>\n",
       "      <td>110</td>\n",
       "      <td>3.839373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6_150</td>\n",
       "      <td>150</td>\n",
       "      <td>1.057269</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID  Depth   Fungi (Spectral counts/L)\n",
       "0   6_20     20                    5.710345\n",
       "1   6_40     40                    3.112392\n",
       "2   6_70     70                    1.380000\n",
       "3  6_110    110                    3.839373\n",
       "4  6_150    150                    1.057269"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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