MixSalt.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import matplotlib as mpl\n",
    "mpl.use('TkAgg')\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import csv\n",
    "import string\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "######################################################################\n",
    "# Plot configurations\n",
    "# mpl.style.use('classic')\n",
    "mpl.rcParams['lines.linewidth'] = 1.0\n",
    "mpl.rcParams['lines.dashed_pattern'] = [6, 6]\n",
    "mpl.rcParams['lines.dashdot_pattern'] = [3, 5, 1, 5]\n",
    "mpl.rcParams['lines.dotted_pattern'] = [1, 3]\n",
    "mpl.rcParams['lines.scale_dashes'] = False\n",
    "\n",
    "mpl.rcParams['grid.color'] = 'k'\n",
    "mpl.rcParams['grid.linestyle'] = ':'\n",
    "mpl.rcParams['grid.linewidth'] = 0.5\n",
    "\n",
    "mpl.rcParams['axes.autolimit_mode'] = 'round_numbers'\n",
    "mpl.rcParams['axes.xmargin'] = 0\n",
    "mpl.rcParams['axes.ymargin'] = 0\n",
    "######################################################################\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "database = 'pitzer'\n",
    "PHdir = '/Users/siavash/Software/phreeqc-3.4.0-12927/bin/Phreeqc'\n",
    "OUTdir = '/Users/siavash/Rice/Oman/BrineSalinity/PHREEQC/PythonEX/OUTputs'\n",
    "INdir = '/Users/siavash/Rice/Oman/BrineSalinity/PHREEQC/PythonEX/INputs'\n",
    "database_path = '/Users/siavash/Software/phreeqc-3.4.0-12927/database'\n",
    "\n",
    "mm = ['o', 's', 'x', '*', 'v', '<', '>', 'h', 'H']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "NaCl  = 1             # Added salt\n",
    "\n",
    "added_salt = 'SrCl2'  # Added salt\n",
    "cat = 'Sr'            # Cation of the added salt\n",
    "ani = 'Cl'            # Anion of the added salt\n",
    "cat_phqc = 'Sr'       # Phreeqc form of the cation\n",
    "ani_phqc = 'Cl'       # Phreeqc form of the anion\n",
    "cat_ch = 2            # Charge of the anion\n",
    "ani_ch = 1\n",
    "cat_mo = 1\n",
    "ani_mo = 2\n",
    "\n",
    "T = 20\n",
    "P = 493.462\n",
    "X1 = 2\n",
    "X2 = 5.5\n",
    "st = 0.5\n",
    "X = np.arange(X1, X2, st)\n",
    "\n",
    "\n",
    "if cat_ch != 1:\n",
    "    cation = (\"%s+%s\" % (cat, cat_ch))                                         # Charged cation of the added salt\n",
    "else:\n",
    "    cation = (\"%s+\" % cat)\n",
    "\n",
    "if ani_ch != 1:\n",
    "    anion = ('%s-%s' % (ani, ani_ch))                                         # Charged cation of the added salt\n",
    "else:\n",
    "    anion = ('%s-' % ani)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def phreeqc_sim(phqc_path, phqc_input):\n",
    "\n",
    "   cmd_string = (\"%s %s\" % (phqc_path, phqc_input))\n",
    "   os.system(cmd_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def nacl_run(phdir, outdir, indir, db_path, db_name, t, p, nacl):\n",
    "\n",
    "    output_file = (\"%s/%sM%s.txt\" % (outdir, nacl, 'NaCl'))\n",
    "    input_file = (\"%s/%sM%s.phr\" % (indir, nacl, 'NaCl'))\n",
    "\n",
    "    #########################################################\n",
    "    input_string = ('''\n",
    "        DATABASE %s/%s.dat\n",
    "        TITLE  Example Temperature dependance of solubility of halite\n",
    "        SOLUTION 1 brine\n",
    "        units    mol/kgw\n",
    "        temp     %s\n",
    "        pressure %s\n",
    "        pH                7   charge\n",
    "        Na   %s\n",
    "        Cl   %s\n",
    "        -water 1 # kg\n",
    "        REACTION_TEMPERATURE  1\n",
    "            20.0 110.0 in 91 steps\n",
    "        SELECTED_OUTPUT\n",
    "        -file %s\n",
    "        -temperature\n",
    "        -si Halite\n",
    "        -activities Na+ Cl-\n",
    "        -ionic_strength\n",
    "        -molalities Na+ Cl-\n",
    "        -state false\n",
    "        END''' % (db_path, db_name, t, p, nacl, nacl, output_file))\n",
    "\n",
    "    input_write = open(input_file, \"w\")\n",
    "    input_write.write(input_string)\n",
    "    input_write.close()\n",
    "\n",
    "    phreeqc_sim(phdir, input_file)\n",
    "\n",
    "    header = ['sim', 'soln', 'dist_x', 'time', 'step', 'pH', 'pe', 'temp', 'mu', 'm_cat', 'm_ani', 'la_cat', 'la_ani',\n",
    "              'si_H']\n",
    "    sim_result = pd.read_table(output_file, delim_whitespace=True, header=None, names=header)\n",
    "    ph = pd.to_numeric(sim_result['pH'][1:])\n",
    "    temp = pd.to_numeric(sim_result['temp'][1:])\n",
    "    I = pd.to_numeric(sim_result['mu'][1:])\n",
    "    m_cat = pd.to_numeric(sim_result['m_cat'][1:])\n",
    "    m_ani = pd.to_numeric(sim_result['m_ani'][1:])\n",
    "    a_cat = pd.to_numeric(sim_result['la_cat'][1:])\n",
    "    a_ani = pd.to_numeric(sim_result['la_ani'][1:])\n",
    "    si_h = pd.to_numeric(sim_result['si_H'][1:])\n",
    "\n",
    "    log_gam_na = a_cat - np.log10(m_cat)\n",
    "    log_gam_cl = a_ani - np.log10(m_ani)\n",
    "    gam_na = np.power(10, log_gam_na)\n",
    "    gam_cl = np.power(10, log_gam_cl)  # 10.^(log_gam_ani);\n",
    "    mean_gam_nacl = np.power(np.power(gam_na, 1) * np.power(gam_cl, 1), (np.divide(1.0, 2.0)))  # (gam_cat.^cat_mo.*gam_ani.^ani_mo).^(1/(ani_mo+cat_mo));\n",
    "    log_mean_gam_nacl = np.log10(mean_gam_nacl)\n",
    "\n",
    "    return si_h, log_gam_na, log_gam_cl, mean_gam_nacl, log_mean_gam_nacl\n",
    "########################################################################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# added salt does not contain Na and Cl ions\n",
    "def mixsalt_run1(phdir, outdir, indir, db_path, db_name, t, p, x, catmo, animo, nacl,ctph, anph, salt, salt_cation, salt_anion):\n",
    "    ph = np.zeros((92, len(x)))\n",
    "    temp = np.zeros((92, len(x)))\n",
    "    i_strength = np.zeros((92, len(x)))\n",
    "    m_cat = np.zeros((92, len(x)))\n",
    "    m_ani = np.zeros((92, len(x)))\n",
    "    m_na = np.zeros((92, len(x)))\n",
    "    m_cl = np.zeros((92, len(x)))\n",
    "    a_cat = np.zeros((92, len(x)))\n",
    "    a_ani = np.zeros((92, len(x)))\n",
    "    a_na = np.zeros((92, len(x)))\n",
    "    a_cl = np.zeros((92, len(x)))\n",
    "    si_h = np.zeros((92, len(x)))\n",
    "\n",
    "    c = 0\n",
    "    for m in x:\n",
    "        xc = catmo * m\n",
    "        xa = animo * m\n",
    "        output_file = (\"%s/%sM%s_%sMNaCl.txt\" % (outdir, m, salt, nacl))\n",
    "        input_file = (\"%s/%sM%s_%sMNaCl.phr\" % (indir, m, salt, nacl))\n",
    "\n",
    "        #########################################################\n",
    "        input_string = ('''\n",
    "        DATABASE %s/%s.dat\n",
    "        TITLE  Example Temperature dependance of solubility of halite\n",
    "        SOLUTION 1 brine\n",
    "        units    mol/kgw\n",
    "        temp     %s\n",
    "        pressure %s\n",
    "        pH                7   charge\n",
    "        Na   %s\n",
    "        Cl   %s\n",
    "        %s   %s\n",
    "        %s   %s\n",
    "        -water 1 # kg\n",
    "        REACTION_TEMPERATURE  1\n",
    "            20.0 110.0 in 91 steps\n",
    "        SELECTED_OUTPUT\n",
    "        -file %s\n",
    "        -temperature\n",
    "        -si Halite\n",
    "        -activities Na+ Cl- %s %s\n",
    "        -ionic_strength \n",
    "        -molalities Na+ Cl- %s %s\n",
    "        -state false\n",
    "        END''' % (db_path, db_name, t, p, nacl, nacl, ctph, xc, anph, xa, output_file, salt_cation, salt_anion, salt_cation, salt_anion))\n",
    "\n",
    "        input_write = open(input_file, \"w\")\n",
    "        input_write.write(input_string)\n",
    "        input_write.close()\n",
    "        #########################################################\n",
    "\n",
    "        phreeqc_sim(phdir, input_file)\n",
    "        header = ['sim', 'soln', 'dist_x', 'time', 'step', 'pH', 'pe', 'temp', 'mu',\n",
    "                  'm_na', 'm_cl', 'm_cat', 'm_ani', 'la_na', 'la_cl', 'la_cat',\n",
    "                  'la_ani', 'si_H']\n",
    "\n",
    "        sim_result = pd.read_table(output_file, delim_whitespace=True, header=None, names=header)\n",
    "        ph[:, c] = pd.to_numeric(sim_result['pH'][1:])\n",
    "        temp[:, c] = pd.to_numeric(sim_result['temp'][1:])\n",
    "        i_strength[:, c] = pd.to_numeric(sim_result['mu'][1:])\n",
    "        m_cat[:, c] = pd.to_numeric(sim_result['m_cat'][1:])\n",
    "        m_ani[:, c] = pd.to_numeric(sim_result['m_ani'][1:])\n",
    "        m_na[:, c] = pd.to_numeric(sim_result['m_na'][1:])\n",
    "        m_cl[:, c] = pd.to_numeric(sim_result['m_cl'][1:])\n",
    "        a_cat[:, c] = pd.to_numeric(sim_result['la_cat'][1:])\n",
    "        a_ani[:, c] = pd.to_numeric(sim_result['la_ani'][1:])\n",
    "        a_na[:, c] = pd.to_numeric(sim_result['la_na'][1:])\n",
    "        a_cl[:, c] = pd.to_numeric(sim_result['la_cl'][1:])\n",
    "        si_h[:, c] = pd.to_numeric(sim_result['si_H'][1:])\n",
    "\n",
    "        c = c + 1\n",
    "    #\n",
    "    log_gam_cat = a_cat - np.log10(m_cat)\n",
    "    log_gam_ani = a_ani - np.log10(m_ani)\n",
    "    log_gam_na = a_na - np.log10(m_na)\n",
    "    log_gam_cl = a_cl - np.log10(m_cl)\n",
    "\n",
    "    gam_cat = np.power(10, log_gam_cat)\n",
    "    gam_ani = np.power(10, log_gam_ani)\n",
    "    gam_na = np.power(10, log_gam_na)\n",
    "    gam_cl = np.power(10, log_gam_cl)\n",
    "\n",
    "    mean_gam_salt = np.power(np.power(gam_cat, catmo) * np.power(gam_ani, animo), (\n",
    "        np.divide(1.0, animo + catmo)))\n",
    "    mean_gam_nacl = np.power(np.power(gam_na, 1) * np.power(gam_cl, 1),\n",
    "                             (np.divide(1.0, 2.0)))\n",
    "\n",
    "    log_mean_gam_nacl = np.log10(mean_gam_nacl)\n",
    "    log_mean_gam_salt = np.log10(mean_gam_salt)\n",
    "    return temp, si_h, log_mean_gam_nacl, log_mean_gam_salt, log_gam_cat, log_gam_ani, log_gam_na, log_gam_cl\n",
    "########################################################################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# added salt does contain Cl ion but no Na\n",
    "def mixsalt_run2(phdir, outdir, indir, db_path, db_name, t, p, x, catmo, animo, nacl, salt, ctph, salt_cation):\n",
    "    ph = np.zeros((92, len(x)))\n",
    "    temp = np.zeros((92, len(x)))\n",
    "    i_strength = np.zeros((92, len(x)))\n",
    "    m_cat = np.zeros((92, len(x)))\n",
    "    m_na = np.zeros((92, len(x)))\n",
    "    m_cl = np.zeros((92, len(x)))\n",
    "    a_cat = np.zeros((92, len(x)))\n",
    "    a_na = np.zeros((92, len(x)))\n",
    "    a_cl = np.zeros((92, len(x)))\n",
    "    si_h = np.zeros((92, len(x)))\n",
    "\n",
    "    c = 0\n",
    "    for m in x:\n",
    "        xc = catmo * m\n",
    "        xa = animo * m\n",
    "        output_file = (\"%s/%sM%s_%sMNaCl.txt\" % (outdir, m, salt, nacl))\n",
    "        input_file = (\"%s/%sM%s_%sMNaCl.phr\" % (indir, m, salt, nacl))\n",
    "\n",
    "        cl = nacl + xa\n",
    "        #########################################################\n",
    "        input_string = ('''\n",
    "        DATABASE %s/%s.dat\n",
    "        TITLE  Example Temperature dependance of solubility of halite\n",
    "        SOLUTION 1 brine\n",
    "        units    mol/kgw\n",
    "        temp     %s\n",
    "        pressure %s\n",
    "        pH                7   charge\n",
    "        Na   %s\n",
    "        Cl   %s\n",
    "        %s   %s\n",
    "        -water 1 # kg\n",
    "        REACTION_TEMPERATURE  1\n",
    "            20.0 110.0 in 91 steps\n",
    "        SELECTED_OUTPUT\n",
    "        -file %s\n",
    "        -temperature\n",
    "        -si Halite\n",
    "        -activities Na+ Cl- %s \n",
    "        -ionic_strength\n",
    "        -molalities Na+ Cl- %s\n",
    "        -state false\n",
    "        END''' % (db_path, db_name, t, p, nacl, cl, ctph, xc, output_file, salt_cation, salt_cation))\n",
    "\n",
    "        input_write = open(input_file, \"w\")\n",
    "        input_write.write(input_string)\n",
    "        input_write.close()\n",
    "        #########################################################\n",
    "\n",
    "        phreeqc_sim(phdir, input_file)\n",
    "        header = ['sim', 'soln', 'dist_x', 'time', 'step', 'pH', 'pe', 'temp', 'mu',\n",
    "                  'm_na', 'm_cl', 'm_cat', 'la_na', 'la_cl', 'la_cat',\n",
    "                  'si_H']\n",
    "\n",
    "        sim_result = pd.read_table(output_file, delim_whitespace=True, header=None, names=header)\n",
    "        ph[:, c] = pd.to_numeric(sim_result['pH'][1:])\n",
    "        temp[:, c] = pd.to_numeric(sim_result['temp'][1:])\n",
    "        i_strength[:, c] = pd.to_numeric(sim_result['mu'][1:])\n",
    "        m_cat[:, c] = pd.to_numeric(sim_result['m_cat'][1:])\n",
    "        m_na[:, c] = pd.to_numeric(sim_result['m_na'][1:])\n",
    "        m_cl[:, c] = pd.to_numeric(sim_result['m_cl'][1:])\n",
    "        a_cat[:, c] = pd.to_numeric(sim_result['la_cat'][1:])\n",
    "        a_na[:, c] = pd.to_numeric(sim_result['la_na'][1:])\n",
    "        a_cl[:, c] = pd.to_numeric(sim_result['la_cl'][1:])\n",
    "        si_h[:, c] = pd.to_numeric(sim_result['si_H'][1:])\n",
    "\n",
    "        c = c + 1\n",
    "\n",
    "    log_gam_cat = a_cat - np.log10(m_cat)\n",
    "    log_gam_na = a_na - np.log10(m_na)\n",
    "    log_gam_cl = a_cl - np.log10(m_cl)\n",
    "\n",
    "    gam_cat = np.power(10, log_gam_cat)\n",
    "    gam_na = np.power(10, log_gam_na)\n",
    "    gam_cl = np.power(10, log_gam_cl)\n",
    "\n",
    "    mean_gam_salt = np.power(np.power(gam_cat, catmo) * np.power(gam_cl, animo), (\n",
    "        np.divide(1.0, animo + catmo)))\n",
    "    mean_gam_nacl = np.power(np.power(gam_na, 1) * np.power(gam_cl, 1),\n",
    "                             (np.divide(1.0, 2.0)))\n",
    "\n",
    "    log_mean_gam_nacl = np.log10(mean_gam_nacl)\n",
    "    log_mean_gam_salt = np.log10(mean_gam_salt)\n",
    "    return temp, si_h, log_mean_gam_nacl, log_mean_gam_salt, log_gam_cat, log_gam_na, log_gam_cl\n",
    "########################################################################################################################\n",
    "########################################################################################################################\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# added salt does contain Na ion but no Cl\n",
    "def mixsalt_run3(phdir, outdir, indir, db_path, db_name, t, p, x, catmo, animo, nacl, salt, anph, salt_anion):\n",
    "    ph = np.zeros((92, len(x)))\n",
    "    temp = np.zeros((92, len(x)))\n",
    "    i_strength = np.zeros((92, len(x)))\n",
    "    m_ani = np.zeros((92, len(x)))\n",
    "    m_na = np.zeros((92, len(x)))\n",
    "    m_cl = np.zeros((92, len(x)))\n",
    "    a_na = np.zeros((92, len(x)))\n",
    "    a_cl = np.zeros((92, len(x)))\n",
    "    a_ani = np.zeros((92, len(x)))\n",
    "    si_h = np.zeros((92, len(x)))\n",
    "\n",
    "    c = 0\n",
    "    for m in x:\n",
    "        xc = catmo * m\n",
    "        xa = animo * m\n",
    "        output_file = (\"%s/%sM%s_%sMNaCl.txt\" % (outdir, m, salt, nacl))\n",
    "        input_file = (\"%s/%sM%s_%sMNaCl.phr\" % (indir, m, salt, nacl))\n",
    "\n",
    "        na = nacl + xc\n",
    "        #########################################################\n",
    "        input_string = ('''\n",
    "        DATABASE %s/%s.dat\n",
    "        TITLE  Example Temperature dependance of solubility of halite\n",
    "        SOLUTION 1 brine\n",
    "        units    mol/kgw\n",
    "        temp     %s\n",
    "        pressure %s\n",
    "        pH                7   charge\n",
    "        Na   %s\n",
    "        Cl   %s\n",
    "        %s   %s\n",
    "        -water 1 # kg\n",
    "        REACTION_TEMPERATURE  1\n",
    "            20.0 110.0 in 91 steps\n",
    "        SELECTED_OUTPUT\n",
    "        -file %s\n",
    "        -temperature\n",
    "        -si Halite\n",
    "        -activities Na+ Cl- %s \n",
    "        -ionic_strength\n",
    "        -molalities Na+ Cl- %s\n",
    "        -state false\n",
    "        END''' % (db_path, db_name, t, p, na, nacl, anph, xa, output_file, salt_anion, salt_anion))\n",
    "\n",
    "        input_write = open(input_file, \"w\")\n",
    "        input_write.write(input_string)\n",
    "        input_write.close()\n",
    "        #########################################################\n",
    "\n",
    "        phreeqc_sim(phdir, input_file)\n",
    "        header = ['sim', 'soln', 'dist_x', 'time', 'step', 'pH', 'pe', 'temp', 'mu',\n",
    "                  'm_na', 'm_cl', 'm_ani', 'la_na', 'la_cl', 'la_ani',\n",
    "                  'si_H']\n",
    "\n",
    "\n",
    "        sim_result = pd.read_table(output_file, delim_whitespace=True, header=None, names=header)\n",
    "        ph[:, c] = pd.to_numeric(sim_result['pH'][1:])\n",
    "        temp[:, c] = pd.to_numeric(sim_result['temp'][1:])\n",
    "        i_strength[:, c] = pd.to_numeric(sim_result['mu'][1:])\n",
    "        m_ani[:, c] = pd.to_numeric(sim_result['m_ani'][1:])\n",
    "        m_na[:, c] = pd.to_numeric(sim_result['m_na'][1:])\n",
    "        m_cl[:, c] = pd.to_numeric(sim_result['m_cl'][1:])\n",
    "        a_ani[:, c] = pd.to_numeric(sim_result['la_ani'][1:])\n",
    "        a_na[:, c] = pd.to_numeric(sim_result['la_na'][1:])\n",
    "        a_cl[:, c] = pd.to_numeric(sim_result['la_cl'][1:])\n",
    "        si_h[:, c] = pd.to_numeric(sim_result['si_H'][1:])\n",
    "\n",
    "        c = c + 1\n",
    "\n",
    "    log_gam_ani = a_ani - np.log10(m_ani)\n",
    "    log_gam_na = a_na - np.log10(m_na)\n",
    "    log_gam_cl = a_cl - np.log10(m_cl)\n",
    "\n",
    "    gam_ani = np.power(10, log_gam_ani)\n",
    "    gam_na = np.power(10, log_gam_na)\n",
    "    gam_cl = np.power(10, log_gam_cl)\n",
    "\n",
    "    mean_gam_salt = np.power(np.power(gam_na, catmo) * np.power(gam_ani, animo), (\n",
    "        np.divide(1.0, animo + catmo)))\n",
    "    mean_gam_nacl = np.power(np.power(gam_na, 1) * np.power(gam_cl, 1),\n",
    "                             (np.divide(1.0, 2.0)))\n",
    "\n",
    "    log_mean_gam_nacl = np.log10(mean_gam_nacl)\n",
    "    log_mean_gam_salt = np.log10(mean_gam_salt)\n",
    "    return temp, si_h, log_mean_gam_nacl, log_mean_gam_salt, log_gam_ani, log_gam_na, log_gam_cl\n",
    "########################################################################################################################\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Run simulation\n",
    "[si_halite_bc, log_gam_na_bc, log_gam_cl_bc, mean_gam_nacl_bc, log_mean_gam_nacl_bc] = \\\n",
    "    nacl_run(PHdir, OUTdir, INdir, database_path, database, T, P, NaCl)\n",
    "\n",
    "if cat != 'Na' and ani != 'Cl':\n",
    "    [temp, si_h, log_mean_gam_nacl, log_mean_gam_salt, log_gam_cat, log_gam_ani, log_gam_na, log_gam_cl] = \\\n",
    "        mixsalt_run1(PHdir, OUTdir, INdir, database_path, database, T, P, X, cat_mo, ani_mo, NaCl, cat_phqc, ani_phqc,\n",
    "            added_salt, cation, anion)\n",
    "\n",
    "elif ani == 'Cl' and cat != 'Na':\n",
    "    [temp, si_h, log_mean_gam_nacl, log_mean_gam_salt, log_gam_cat, log_gam_na, log_gam_cl] = \\\n",
    "        mixsalt_run2(PHdir, OUTdir, INdir, database_path, database, T, P, X, cat_mo, ani_mo, NaCl, added_salt, cat_phqc,\n",
    "            cation)\n",
    "\n",
    "elif cat == 'Na' and ani != 'Cl':\n",
    "    [si_h, log_mean_gam_nacl, log_mean_gam_salt, log_gam_ani, log_gam_na, log_gam_cl] = \\\n",
    "         mixsalt_run3(PHdir, OUTdir, INdir, database_path, database, T, P, X, cat_mo, ani_mo, NaCl, added_salt, ani_phqc,\n",
    "            anion)\n",
    "else:\n",
    "    print(\"Added salt is the same as NaCl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IwHUA0zjnF2vrt2XLlvVvrJ8Q2joQMcM74PQ3OeBWKa95cACO773odOMC7DdXe576hij/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\niVMyICIi0sT9fzSua/yI93vbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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sIsSYbU14RPeLkWMEjJhQjMPveQzfMSYwTP36Zaiurs7YXfFAcsFk0k4XGzkv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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ZqCZeiLffRVvp7jqC09pG2ECoO2roiGUFwzsHKuZA0R2RUuqR90GDX1Fv9Osd\nAC/+Cg9Sk5iTbZiTbJiTrJiS9POK9d82fnBYvneFIla0ZZ8DRexQMQcKRSfQkRvvSL+mT+NXRI7y\nIx0AfS1f2MyYk6wBo2/DlGzDnGrH1j8p5ACYk21NvnoX4Rx043frFQpF52N450DFHMQX3UHH6Tbe\niaZBc/sbGXqtsrEDoNX69Wn8wEjflKifW/u4cAxLqzf6STaENQav4Rn83XojaADj6OiOU/INMYKG\nWGB450ChaBMNN94ZloZjaCrSp9Fzv+BU4X7dAajSp/aRMsywB6b2k23Ye6boI/7g6N9l7bR1/DPt\n3XqFQhE7VMyB4oxE+jV9VF/uwV/u1qf0A8faf0R/rcrksuIc37ve0Ldgal+hOBNQMQddi4o5UCha\ngObx64a+3IO/ItrRjVbj06f3A5H7lhQ7pmQbtr6Jkc6BesdeoVCcgcTH3qIdiJFiDoxAe3RIKfFX\ne/EUVlH7VQlVW4soX3+Ykj/9k5O/3813//0Fx5ZvoXDF5xS/vJeKj49Q989StDoflp4JuMb1JvXS\nIWTcPJZ+K86j739OJuOmPHr+ZCSplw4heWp/nGN76x9mFmAx4ZrYh8w7J5J22dCQY6B+F/GDETSA\ncXTE+9/bxx57DCEExcXFUa+vWrWKIUOGMGzYMFatWhWqz87O5vzzz49om5eXR25uLqDvfHjllVcy\nevRocnNzmTJlStSdE0HfEvn6669nyJAhjBo1igsuuICtW7cCepIm0FORn3vuuYwaNYoxY8awZs2a\ndmtvLWrmQBFT2hrpL/0Sf5WnfsQfMdUfHPV7EFaTHswXGPGbU+zYBiRhTumJOVmvMzkt7ZriV2v1\nCkXHsWblNooLGhvOnlmJzLt7Uruf39T2yUePHuXDDz9kwIDob+uUlJRw33338cknn5CcnMz48eO5\n9NJLQ2mXKysrOXr0KP3792f//sg8D08++SQZGRns3q1v933gwAGsVmtEGyklUkoWLVrEoEGDOHjw\nICaTiUOHDjV6ntPp5JVXXmHYsGEUFhYyfvx4pk+fTmpqanu+mlZheOfAKFGn3SWSOVqkv3CYQ4Z+\nYspIKjYebbTOr1V766P4k+2YU3TDb810RTgDJpu5wzWcLi1td/ldnA4j6DCCBjCOjpb8ve0zOJmS\nomo0f328m8ks6DMkpSO7xi9+8QseeeQRfvSjH0W9/sEHHzBt2jQGDhwIwLRp03j//fdDWyXPnTuX\nNWvWsGTJEl5//XUWLFjAq6++Cugpm4P3AQwfPhzQMyrOmDGDiy66iC1btvDEE0+wdetWXnvttVB6\n58GDBzN48OCIvpx11lmh877WUfA5AAAgAElEQVR9+9K7d29OnjypnANF90H6NX1UX+bGVx5IwxqM\n9N9aRPXWIhBgSrFjSbMHDL8dS48E7INTQ06AOdGGMKuAPoXC6EyYOYj9W74LZdkEECbBhB9kd9hn\nvvPOO/Tr14+zz256869jx47Rv3//UDkrK4tjx46FypdffjnXXHMNS5YsYd26dbz22msh5+BnP/sZ\nF198MX/605/Iz8/npz/9aSiXw4EDB/jf//1fnnnmGd555x3y8vIwm1s+yNm2bRsejyeUHbKzMLxz\nEO9rYC2lK96DllIi6/z4ytz4y+p0B6DMjT/0U4e/yos5Ud+cx5xqb/JZljQ7va8/2xDvcxtBAxhD\nhxE0gHF0BPcIePqGDa26z+/VePnOzc22uenZ7zV7vamUzffddx8PPPAA69evb/b+4Jt74fschC9P\npqenk5aWxurVq8nJycHpdIau5eXlcejQIdavX89HH33ExIkT2bJlCwkJCQwcOJBzzjmn2c9uiqKi\nIn7yk5+watWq0ExDZ2F450DRNNIv8VcGDH2pO6oTgARzmh1LwPibUx3YRrj08zQ75iR7xIi/oJlI\nf4VCcWZwOkNeXe7m1bu34PdqmK0mfrLyXFwpTQ8uWkIwqK9hzMHu3bv55ptvQrMGBQUFjBs3jm3b\nttGnT5/Q/VlZWRGBoQUFBY0ctnnz5nHTTTdFTQedmJjI7NmzmT17NiaTiffee485c+bgcrlCbUaN\nGsU//vEPNE07rbGvqKhg5syZrFy5ss3ORXswvHNwJsccaHW+MEMfMPqlYSP/Kg9mlxVzmiM08rf2\nceEYkY451YEl1Y5wtOH9/WZe/zPC6MgIGsAYOoygAYyjo6V/b10pdnLO7cOezwrJ+bfMdjsGzTF6\n9GhOnDgRKmdnZ7N9+3Z69uwZ0W769On853/+Jz6fj9LSUtavX8+DDz4Y0WbWrFkUFRUxffp0CgsL\nQ/WbN29m5MiRpKWl4fF42LdvX9Tf6ZAhQ5gwYQL33nsvK1asQAjBwYMH2bdvX0QshMfjYdasWVx9\n9dVcccUVMfomWofhnYPuQmuj/PVRv6fJ6X5fmRs0iTlVN/zBkb9jRDqWlMAsQIoNYY7tVJWK9Fco\nFC1hwsxBlBRVd2iswekIT9mcnp7OsmXLmDhxIgD33HMP6enpEe2TkpK48847Gz3n66+/5uc//7me\nME3TmDlzJnPmzOHbbxsnP3vxxRdZvHgxQ4cOxel00qNHDx599NGINmvXruXTTz/l1KlToVmKl19+\nObRU0hkYfofE7pKyuWDpZ/Uj7vEZJJ7fF/wyNNI/tOufZCVn1DsBlR5MLmvEdH/9ue4MiIT2vdLX\nERhhbdUIGsAYOoygAbq/DpWyuWtROyQaCM3tx19ah6+kDl9pnV7ZIMpfOMxY+yZi7ZmANIN9WBrO\nVDuWNIe+Za/F8HtYKRQKhaILMPzMQVflVpBeDV9ZnR7oF3AAgs6Av7QO6dH0QL80B+Y0B9WfFzV+\niABbdjK9r2/69RuFQqHoalRuha5FzRzEEdIv8Ze7Q8beV1qHv6QOX6kbX2kdWrVXn95Pc+gOQLod\n28gemNMcWNIdmBKtEVP+Ec6BivJXKBQKRRdieOegrfscSC0Q8Bcc7QcMf6hc6dEz8gVG/5Z0B/Zh\nabjSHJjTA9P+rU3N20yUf3dfkwxiBB1G0ADG0GEEDWAcHd1xvb4hRtAQCwzvHFhqwV/hafQGgJQS\nrdobGPm7w0b+gXJZHaYES2DUr4/+7QOTMY/tpdel2GO65q+i/BUKhUIRLxg+5mBM5gj53s9exNYv\nEXOvBGS1L7QUIKym0DS/Oc2hb+8bcAQsaXaEteP38VcoFIrujoo56Fo6IubgzAh390s8RyrxfFOB\na1If0heMIPPXk+l7z7lk3DKWHlfmkPqDQSSe25eE4elYezuVY6BQKBQG43Qpm81mM3l5eeTl5XHp\npZeG6qdOncqAAQMIH0xfdtlloRTLmqZx6623kpuby+jRo5k4cSLffPNN1M/wer0sXbqUYcOGkZub\ny6RJk/jrX/8K6Bs0FRcXc/ToUS666CJycnIYNWoUTz75ZKy+ghZzZjgHFhOuczLp/fOzSRjZA1um\nC5Oje62oGCXfuxF0GEEDGEOHETSAcXS0JcarqrSEj158mlfuvDUmfdi4cSPXXHNNo/rTpWwGSEhI\n4LPPPmPnzp288847EddSU1PZvFnP/1BWVkZRUX0Q+Zo1aygsLGTXrl3s3r2bP//5z1EzKPr9fpYt\nW0ZRURF79uxhz549rFu3rtH3ZrFYePzxx9m/fz+ff/45Tz/9NPv27WvN19BuupeFbAN+qyTzzolq\nHV+hUCjiiKrSEj5/83X2bPwYpIbf5+vQzztdyubTMX/+fFavXs2UKVN46623mD17Nnv37gX0BEmZ\nmZmhfAlZWVmh+xITE7njjjv44IMPePTRR3nhhRf45ptvsNv1LaMzMjKYO3duxGdlZmaSmZkJ6Lsy\n5uTkcOzYMUaOHNmmvrcFw88cJPROMoRjYIRIZjCGDiNoAGPoMIIGMI6OlkT5B2cKXrxlEXs++RC/\n19PhjkFLUjYD1NXVcdFFF3HOOefw9ttvR1zLz8/n008/xe/3s3r1aubNmxe6NnfuXNatW0deXh6L\nFy9mx44doWvV1dXk5uaydetWUlNTGTBgAMnJyS3u++HDh9mxYweTJ09u8T2xwPAzBwqFQqHoXB6f\nd0nM71u85t1m721vymaAI0eO0LdvXw4dOsT3vvc9Ro8ezZAhQwA9HmHKlCmsWbOG2tpasrOzQ/dl\nZWVx4MABNmzYwIYNG8jPz+eNN94gPz8fs9nMnDlzWqC+MVVVVcyZM4cnnnjitA5FcCam8OABrn74\nt236vHAM7xy0dZ+DeMMo70EbQYcRNIAxdBhBAxhHR3CPgOYMeXVZKVvefJ29Gz9CapHLCadzAJqj\nrSmb/T4f1aUleNx19M0aQGVlJYMHD2bq1Kns2LEj5ByAvrQwa9Ysli9f3ujz7XY7M2bMYMaMGWRk\nZPD222+Tn5+Pw+HAbNYD3IcOHcqRI0datJeC1+tlzpw5XHnllcyePbvJdn6fj7rKSl68/9cxXZ4x\nvHOgUCgUio4hOFpNGdnybIGu1DT+feGNnDtnQZNOQixpKmVzWmoqFSdPUFtZAUBpWRlJvTIAKC4u\nZvPmzfzqV78KvaGgaRrn/du/ceeddzL3iivwe70A+DwevvzySzIyepOZ2RfN72fHji8ZPSoXd3U1\nAHVVlUgJAsnVV13FjTfcwG+f+G9sVitFRUVs/HQT86+Yi9Q0qkqKsUqNG266mcHZA1l41ZWUHf8O\nkCAlUoJEBr4zL5rPj8ddi9/rien3ZnjnwCg7XRlhVAHG0GEEDWAMHUbQAN1PR8NgwnPPygVa9/e2\noZNQ+M+vIq5LKQM/GmgyrBw0klqDskRqktrKCrxuN5WniqO21fx+Th49jK88hZ27d/PqH1/n8Qcf\n4ODXXzPrx1diEiY0TePGhT+jh8PG8UP/wlNXS2nhMYp79eCauZdDXQ0lhbVIKSn7rohvDh7g2uuu\nxeP2gIBxeXlcPX8eNRVlICV1VVUgBELAf/5yCSsfepjxkybjcDhwOp38+q6lId3CbGHb9i9Y/cYb\njBo1kvP/fRoA9917DzOmfx8ECCGoPHUKzecPfFnt/IVGwfCbIHVV4iWFQqEwCn6fF09dHeXHv2P7\nu3/mX9v+L2RoAabc9EvGjB0bZqw1pNbQmEcx6FpYeykjHQIJwmRCCKHnoQkchdDrMInQtWA9YWWE\niLg/WFdVcgqfJ/oou9fAQbrxRYSMOYiIPDjxQnA5pLaygsPHjvG3px4NXVu85l2VeOl0qJiD+MII\nOoygAYyho7tqaBg8FgsdUkp8bjdedx2eujq87jq8dYEfdx3eulq8bjeeulq97HbrdXWBY/h97vo6\nb10dILA67HjdbrQmpv89tTV4fT5sNnu9QTaZMAkTwtTQwDc25pEG39RhBtlqd4SMavB7C2K2WLpN\nbgWzxUJyr9640tIpPFWCxWaL6fKM4Z0DhUKhiBeqSkvY8qc/snfTx0hNQ/P7Of7N11QWFvDNzi8i\nDXhdYyPdyPCHOQA+jwez1YrV4cBqd2C127E5ErA67FgdCXpd4JrN4cDudJGU3qO+PnQtAUvwXrsD\nq8OO2WIFmg8mTOndp1sY1nCj2pST0J0wWyw4EpNY9LvfR12eaStqWUGhUHQbYv26VmuQmoanrhZ3\nTQ2emmrctbWBYw2emprAMaxcU4Onthp3TS11VRVUlZXijzKd3WvgoIDxDhrkgDEPGnmHbsx1g++I\nqNev6fdY7HZMps7Z9r2hk3Du9b9g8gUXdspnx5rwtxV6ZjW9e2I80xG5FdTMgUKhiHvas5uelBKf\nx42nthZ3TXWY4a7Ry7V6ud64Rzf63jo3Frsdu9OJLcEZdnRFlFN69cHmTIio//j3z1J+8kTU/l39\nyO9i9TV1Gg2DCU2W7mtKgjMJiki672+0haiYg/jCCDqMoAG6h47KklP839rX2P+3T0LT8EF2fvD/\n2L93D1l9MhoY+9oIo++prUGYTAFjndDImAfLrpRU0vr01eudTuwJLv0YaGdLSGjzyHzWnfc0+9pe\nd/hdRCPoJOzfvx+gWywrnA4jaIgFhncOFApF1+P1uHFXVVFXXUVdVSV11dXUVVXirg6rC1wPbxdc\nD45G8dHD+N112BKcJKb3iG70A+cWq7UT1Tams9/tVyjai4o5UCjOENq7Xq/5/QGjXaUb9arKMGMf\nVh/F2EupYXcl4khMwuFKxJGYiMOViD0xEYcrSS8HrtnDrvt9Xrb95U8x302vqwmu2Rf+86tOj53o\nCKKteccTy5cv54UXXqBXr14APPDAA/zgBz9o1O7999/ntttuw+/3s2jRIpYu1fcfmDp1KocOHeLb\nb78NvUVx2WWX8dFHH1FVVYWmadx+++1s2LABIQQOh4O1a9cyaNCgRp/h9XpZtmwZb775Jna7HafT\nyX333ceMGTNCGzTV1tZy9dVX891332Eymbjuuuu47bbbmtSnYg4UCkWribZeX37iO914Bw14dRW1\nwZF8mLEPXqurqsTrdmN3ukKG25GYpBvywHlSeg96DcgOGPxIR8ASeL2tLRhxxB2cSTgTOf7kl9gG\nJJOcPwBzcuyT4jXcPjnIL37xC5YsWdLkfX6/n5tuuokPP/yQrKwsJk6cyKWXXhrKhBhM2TxlypRm\nUzabTCYKCgpwuVxRPyM8ZbPdbuf48eNs2rQpol0wZfO4ceOorKxk/PjxTJs2rVOzMhreOVAxB/GF\nEXTEiwapadTVVFNbUU5tRQW1lfpPTUU5tZUVVBafpOhfB6g8VQwNZgjXrvhPvBJ69ekTYeDtrkRS\nMzLD6hJDo3qbIwFh6ppErk3tphcvv4v2YhQdLcoZUFSN90QN1V8cxzU+o8OchNaybds2hg4dSq9e\nvbDZbMyfP5+//OUvIYN8pqVsNrxzoFB0B4Ib2AQNe+inooLayujG311dhc2RQEJyMo6kZBKSknEm\np5CQlIwrJZVDX2yjsriYaHurXvvUS93SIJ3JI25D4ZeApHr7d53mJDz11FO88sorTJgwgccff5y0\ntLSI68eOHaN///6hclZWViiZE+gpm6+99tpQyubnn3+e3/zmN4CesnnKlCl89tln5Ofnc9VVVzF2\n7FigPmXzihUr2LVrl0rZHC8YJeq0u/0Rb4ruqKPhWn1LNPh9PuqqKnVDXhFu7CONf9DQ11VUgBAk\nJOtGPqGBse89aLBeFygnJCXjSEzC3MwrZCMv+F6zU/Hd8XfRECNoAOPoCP69LVj6WctuCDoJW4uo\n3lrUbNOsh85v9npTKZsffvhhfv7zn7Ns2TKEECxbtozFixfz0ksvRdwfjL8LtxnhS2HxnrI51hje\nOVAo2kr4Wr3+Gp2PYwf2Bwx7mNEPju5D5xV43XU4EpMCBj3S2Kdm9CFz6Fn1xj5w3Wp3xLT/KkJe\n0VU0Z8gjHAezvqWya0Jg5iCp7TMHTaVsbsi1117LJZdc0rjPWVkcPXq0vp8FBfTt2zeiTTymbO4o\n4so5EEJ8H3gSMAMvSikfitJmLrAcfa70H1LKHzf3TBVzEF/Eiw6/z0dNRRk1ZWXUlJdRXa4fa8pL\nKT9xnO++PkhVSQkNp+Q3vfoiNR4vWdmDQ0Y9vV8WCUkpAeOfTEJSCnans8vW5xti5PV6I2gA4+ho\n8R4BMXQKWkIwJgDgz3/+M7m5uY3aTJw4kYMHD7J7926GDx/O6tWr+eMf/xjR5vzzz+euu+5iwYIF\nEfVffvklffr0oW/fvmiaxq5duxgzZkyjz3A6nSxcuJBbb72V5557DpvNRlFRER9//DFXXXVVqJ2U\nkoULF5KTk8Mdd9wRi6+g1cSNcyCEMANPA9OAAuDvQoh3pJT7wtoMA+4CzpNSlgoh1LZWihA+rzdg\n4IMGvzSK8dfPPTXVujFPScWZkoorJRVnahqutB4c2rGdqtLGjgHAj1c+3m3/kKv1ekU8YM10YRuY\n3ClOQZBf/epX7Ny5EyEE2dnZPPfccwAUFhayaNEi3nvvPSwWC0899RSzZs1CSsnPfvYzRo0aFfEc\nIUTUNx5OnDjBtddei9vtBmDSpEncfPPNUfuycuVK7r77bkaOHInD4cDlcrFixYqINps3b+bVV19l\n9OjRoeWRpl6/7CjiZp8DIcS5wHIp5fRA+S4AKeWDYW0eAf4ppXyxpc9V+xx0Le19t97rcTcw8LrB\nb2jsa8pL8da5cSYn40xJw5kaMPjhxj+s3pGU1ORud80ll+nO79YrFB1FvO9zYHSMvs9BP+BoWLkA\naBieeRaAEGIz+tLDcinl+w0fJIS4DrgO9NdEwkd6GzduBFDlDi5POHsMn7/5Ors2rAcJUvOHrvu9\nHsaPGUNNeSnbNv8Nb00NWRm9qS4v49t//RNfTQ1WIakuK8PrdmNJcJKe0QdXairlNbVYE1zkjBlD\nv+E5HDz8LckJTqZOuxiHK5FNn37aqD9VwITzW9f/4DT8n377GKe+2otJ6EsR8fL9qrIqx1M5IyMD\nqF/GDS4tqHLnlIM0/P20h3iaObgCmC6lXBQo/wSYJKW8JazNu4AXmAtkAZ8BuVLKsqaeO3z4cHng\nwIEO7XtnsDHOp7L9Ph815WUUf3uYL99fx5E9O5FSIjUt1CYlow+Vp05hEgJnapRRfUoqroj6NOwu\nV4fldW8pDXezi/ffRUsxgg4jaIDuryM4cjVCXoLuqMHoMwcFQP+wchZQGKXN51JKL/CNEOIAMAz4\ne+d08cxD8/upKS+jqrSE6rISqkpK9PPSU4FjKVWlp6irqiQhOQVPbQ3eurqoz5pz1318uXsP35t2\ncZcb/Nag1uoVCsWZRjzNHFiAfwL5wDF0g/9jKeXesDbfBxZIKX8qhOgJ7ADypJSnmnquijmIjqb5\nqa2ooKrkVIThry4toar0FNVlpVSVllBbUY4jMYnEtB4kpqfjSkvHlZpOYlo6ienpJKb1wJWqj/pN\nZrNar1cozkD279/PiBEjupXTbxSklHz11VfGnTmQUvqEEDcDH6DHE7wkpdwrhFgBbJdSvhO4drEQ\nYh/gB37ZnGPQnWlrIJ/UNGorK6gKGvnAyF43+gEHoKyEmvJyHImJuFLTSExLxxUw/r2yBzNo7AS9\nLj0dV0oaJnPL09Sqd+sVijMPh8PBqVOn6NGjh3IQOhEpJadOncLhiO0eKRBHMwcdRXeLOYiWJGfx\nmnf55JNPmDx+XL2RDxn+yCn+6rJS7E5nwODrP4lpPULGPjE1OPpPxWzp+DS2RlyvN4IGMIYOI2iA\n7q/D6/VSUFBAWVlZhxiqzqSurq5baXA4HGRlZWFtkJbcMDMHZzJedx3HDx3i7+v+xOGdXzQK5Hv+\npv+gqqSEfWFGPzGtB660NHr0y2Jg7tmBunScqWldnrs+HLVer1AYH6vVyqBBg9i4cWMop0B3xQga\nYoHhZw66OubA666j8tQpqkqKqTyl/4SfV54qxuvWA/j8Xm/UZyz63e9xpaZhsXV95jKFQqFQxD9q\n5qALiWb4K0+dpKrkFJXFJ6ksOYXXXUdSek8Se/QgqUcvktJ70LN/Ntl5E0jq0ZOkHj1JSEqmprys\nyTX6lN4ZXahSoVAoFGcahp85GD58uPzi8y2tDu4LGv6QsW+h4U/q0YvEgNEPGv7WBOhEi/ZfvObd\nbr8mGcQIOoygAYyhwwgaQOmIJ4ygAdTMwWnxVFXw4i2LQsF9EMXwF5+kMjD6rzpV3KTh7zVgEIPG\nTmyz4W8JTSXJUSgUCoWiszD8zEH/9FR5+7QpobLDlYjX4475iF+hUCgUinhBzRy0kv944jll+BUK\nhUKhaIb4SDjfiTiTU7qlYxBMqNHdMYIOI2gAY+gwggZQOuIJI2iIBYZ3DiwJCVhsNsyWM26SRKFQ\nKBSKNmH4mIMJEybITR99GLFLn0KhUCgURkbFHLQAtUufQqFQKBQtx/DLCpWVlV3dhZhglHUwI+gw\nggYwhg4jaAClI54wgoZYYHjnQKFQKBQKRes4I2IOujK3gkKhUCgUnU17Yw7UzIFCoVAoFIoIDO8c\nqJiD+MIIOoygAYyhwwgaQOmIJ4ygIRacEW8rKBQKhUJhJNas3EZxQVWj+p5Zicy7e1K7n69iDhQK\nhULRIjraIHUGRtAAsOmPX7FvcxGav96Gm8yCkVP6cuGC4SrmQKFQKBSdQ5/ByZjMkdvPm8yCPkNS\nuqhHrSceNUgp9R9NogV//Br+4I8v8OPV8Hn9+Lx+8qYNQJgidQiTYMIPsmPSJ8MvKxgp5sAIOcaN\noMMIGsAYOrqLhtONVuNZh6ZJ/F7dOI08vx/7/68o4roQMDC3B0Vfl/PlF19y9pizA8YtzNhFO/fr\n5WB9RJ2/3khGPQ88I9yYNrymNfyMwLnfq0WMtgE0v+Sf277j4N+P4/P5sAS225cy+B+Qof8Ez8ML\nDa/L0Hn4abRnNUJAyOQLEXYefl0v+P1a6DaTWZDzb5m4UuxNPLh1GN45UCgUiq6mz+BkSoqqG00B\nNxytSikjjLHfK+tHjYEfrYl6vy/svrCRpt8nm6zTTne/Tx/Rmi0m/cdqQpgF+Op1WOxm/v7uN5jM\ngopKyfZjhzGZBMJkwmQWgXNRfx44hurD68x6ndlmCp2H10c8r7nnNnhew/Ot6w7xr+0n0PwSk1lw\n1uQM/m32UASCv23+G1OmnBvSJ0TwPwH7HDZYDyXxCz+EzkUDgx7+wAZt25gMsLrczat3b8Hv1WI6\nawAq5kChUMQx8bA+rPk1fB4Nr8ePz6Ph8/gjzuuPfrxN1NVVezm6v6TRaNGZYkNqMsKACyEwW0SE\nQQ6dW0SDsinU1tSwXXib4DVrg3pL0/WmwGeZTCLCeIUbJLPVxE9Wnhuz0WpnYQQNQTb98Sv2fFZI\n7gX9uHDB8FC9yq2gUCgMS3Mjbil1gxpupL1uf/25R1+bjbje0HC7A2u4EYY/0sBLCRabCYvNjDVw\njDyvr7PYTFhtZmwJZpzJtrD7zJhMgqP7S0Kj1SHjenPOZYMbGXCTKb5TyrtS7OSc24c9nxXGdBq7\nMzGChiATZg6ipKg6prMGcAY4ByrmIL4wgo7uoiHe1rn9Xg2P24e3zo+nzo+3zofH7Q+U9Xqv26df\nC9TXVHrQtMbrw/u3FLH302NgApvDgjXMOFus5qjGPFiXkGTFanNEGPNoBj5YZzKLNk/7hpMxOJlX\n794CfokwCc67fGjIKHWXf1NBmjJI3UmHETSA7ujMWjw+5s81vHOgUJyptHSduyk0TeoGvC5gwAOG\nPfw8aNR1Ix953vA+AKvDjM1u0Y8OM1aHBZvdjDXs3O60kJTuCLU1mwVH9tWPuIdNyuDC+cMxW018\n+ukmpk69oEO+v1hjpNFqRxmkzsQIGjoSFXOgUBgIKSVetx9PrY+y4zWs+90/Ip0Dk2D0Rf0A0Xjk\n7q4f0Xvr/Ph9GlZ7wGg7zJHnDY186DxwjNLWbG3bm9NGWh+uLnez/sU9XLwot9tqUHQPVMyBQhFj\nujIITmoSj9uPu8aLp9aHp9aHu8aHO3AML3si6ryBOj9mi8CeYMHmtGJ3Wqmt9OgPF5CW6QwYcAs2\nh7OBkbcEHAD93GIzxWQ6vb2oEbdC0fkY3jlQMQfxRXfQcbrp+OY0aJpsZNQ9NT7ctV48tf56Ix5m\n8IOGPvhjsZuxJ1iwOy3YEiwBQ2/BnmDF7rTgSrWT3teFzaG3CbULHM3m+hF6xKjbYuKHt+ZFrnNP\njK4j3jDK+nBTKB3xgxE0xIJWOwdCiGnAXOBpKeVOIcR1UsrnY981haLzkVJydn7/Rhu9ADgSrWx9\n5xBFBzU++mZfwLh7IxwBn9uP1RFu0AMGPEE33LbAero9Szf2trBrdqc+DW8yx27jUqOMutWIW6Ho\nXFodcyCE+DPwH8DdwHvA5VLKGzugbzFBxRycufi9+vvlET9VXtw1PuqqIutDdTXe0KtkPk/97mOp\nGU4G5vYIGPDI0Xr9uRWb3dxoS9OuRq1zKxRnHl0Rc3BSSlkGLBFCPARMbOuHK4xHR6zXa35NN97V\nXuqqfWFG3hsw6PXGPlRX7UXzS+wuKw6XFYfLEjhaA3UWUnonhOocifr6vMNlwWIzNwqCu+yOsd3W\nsKpRt0KhaC1tcQ7eDZ5IKZcKIW6JYX9ijoo56FxOt2nNhg83MmHs5JCRd4eN6ING3h0x2tej6G0J\n5npDHjLyusHv0dfVqM6RaMVqN7c5oK656fju8rs4HUbQYQQNoHTEE0bQEAva4hzMCsQdfAn8HXgq\ntl1SdBe8Hj+1lR7qqrzUVOhHu8tKw6UqzS85+Pfj7P2sEGGSHNu4E0eiPkq3hxn7lJ4JZAxMiqhz\nJFqxJVi6ZNe4jtp5TKFQKOKdNu1zIIRIAMahLymMlFJeF+uOxQoVc9ByfF4/tZVeais91FYFjs2U\nNb+GM8mGI9GqH5OsJFnEED8AACAASURBVCTaKPq6jJNHKpGaPmsweGwvplwxDIfLitmisoQrFApF\nR9PpMQdCiAeBZVLKzUKILUBSWz9cUU9HrNX7vRq1Vc0beL2s1/l9GglJNhKSrCQkWvXzRBsJyVZS\nezv1+jBnwOqIPm0fWq/X9ExhU64Y1m3X6xUKheJMpC3LColSSh+AlFITQvwXsDC23Yod3SXmoCXv\n1p8/5QLdsFfVG/S6Ki81lR7qKj3UVHqpqwocKz34vBqOkJG3hhl+G8k9kwPlwLVkG7YmjH1rMfp6\nvRE0gDF0GEEDKB3xhBE0xIK2OAdag3Lj4a6ixXjqfNSUe+if04N9myPfrZea5Pg3Ffzhni1Ulmrs\nX7up3tiHj+6TrCRnJ+uj/LDRvd1p6bId7tR6vUKhUHRf2rLPwVxgEvAE4Acel1L+uAP6FhO6IubA\n79eorfBSU+GmpsKj/5S7qSn3hMrVgSNS4kyx40q2UV3mpqKkDiQIE2SNSGfCjOyQwbcnWOLuHXqF\nQqFQxB+dHnMgpVwrhDiCvgmSHVjZ1g/vDE58W8nTN2wA2rd+L6XEXeOLNPYVnjCDr5eryz14anz6\nunyKDWeyPXC0kZLhpO9ZaTiT9bIzxRbxul34u/Ums4n8n+aotXqFQqFQdDptza1wgZTyhpj2pINp\nKlWtz+sPM/gNDH9wlB8oWywmnCn2kGEPGvm0TBeuFFvIGXAkWtv06p3R1+rBGDqMoAGMocMIGkDp\niCeMoCEWtNU5+JEQogb4UEp5IJYd6jCk/r79+t/vjTD+Xrc/bCRvD5336JdI/5E2XIG6hGQbVpu5\nw7up1uoVCoVC0dW0JeYgEz0ocSxwNjBMSrmoA/oWEwb0Gi7vvPx/6NHXxfDJmfroPjDqdyXb9aA9\ntY6vUCgUCgPRFbkV7pdS/kwI0QP4EXB/Wz+8s2iYqlahUCgUCkXTtGW7urLA8WLgPGBW7LrTMXTn\nVLVBNm7c2NVdiAlG0GEEDWAMHUbQAEpHPGEEDbGgLc6BRQhxN3BE6msS1THuU0wxWVHr9wqFQqFQ\ntIIWxxwIIfpKKQuFEFZgCvB/Ukq3EOJpKeVNMemMEN8HngTMwItSyoeaaHc58AYwUUrZ7CYGKreC\nQqFQKM402htz0JqZg/eEEMsBi5TyEymlGyCGjoEZeBqYAYwEFgghRkZplwTcCmyNxecqFAqFQqGI\npDXOwQSgHNgqhLi6A/oyCfiXlPKQlNIDrEYPeGzIb4BHgLqWPLS75FY4HUZZBzOCDiNoAGPoMIIG\nUDriCSNoiAUtdg6klD4p5X8DFwLjhRD/J4Q4P4Z96QccDSsXBOpCCCHGAv2llO/G8HMVCoVCoVCE\n0WLnQAgxWAjxc+BeYDgwFPhfIcS3QohNMehLtM0GQgERQggT8N/A4hb09TohxHYhxPby8vIIT3Dj\nxo3dshzcsSte+tPWcrAuXvrTlnJDLV3dn7aWp06dGlf9aUs5WBcv/WlrWf3/HT/lhlq6uj/t/X20\nldYEJH4DPAfsBf4JfB1M3SyEGCil/LZdHRHiXGC5lHJ6oHwXgJTywUA5Bfia+iyQfYAS4NLmghJV\nQKJCoVAozjQ6MyBxmpTyISnlOinlgaBjANBexyDA34FhQohBQggbMB94J+wzyqWUPaWU2VLKbOBz\nTuMYgIo5iDeMoMMIGsAYOoygAZSOeMIIGmJBa2IO/tWRHQk4GzcDHwD7gbVSyr1CiBVCiEs78rMV\nCoVCoVDU0+rcCt0NtaygUCgUijONzlxWUCgUCoVCcQZgeOdAxRzEF0bQYQQNYAwdRtAASkc8YQQN\nsaDFWRmFEHdEqS4HvpBS7oxdlxQKhUKhUHQlrXmV8Y/ouySuC1TNRH/DYATwhpTykQ7pYTtRMQcK\nhUKhONPozJiDHsA4KeViKeVidEehF3ABcE1bO6BQKBRnCt4TJzh81U/wnTzZ1V1pF0bQYQQN0HE6\nWuMcDAA84X0CBkopawF3THsVQ1TMQXxhBB1G0ADdS0dTfwC7kwaA4mf+h9ovvuDk089E1CsdnY8R\nNEDTOtpLi2MOgD8Cnwsh/oK+1fElwOtCCBewL6a9UigUijDC/wBmLr+3S/sipQS/H+n1In0+/Rjl\nh7DrR667Hrze0DPKVq+mbPVqsFjoc+89JOz/ipLCQvBrIDWkXwPNX3/UNPBrSM2vtwld0wLX/KFr\nUmoN2viRWqDP4W01Wd8meC3iefox1B+/H8+3kfvdhXQAlr6Z9Kxzc9BhR4Tvhi+aOG/uWsNmMXye\n5+tDELacHtIgBAl5eaRVVfHt/76MMJvBbG5wNCHMFoTZBKGjGWEyg0U/CosZoh3D7m/yOWGfJcxh\n94fXBY6H583X/5010CHsdkb8o/1hgK3a50AIMR6YEviq/3a63QnjARVzoDjT8Z44wbE7FpP13/+F\npVevru5Os0gpdeNaV4d0u/lX/r8jPZ7GDS0W+v3X49DIODcoR1z3IH0+3Wi3qL0X6QsYek/kdYRA\nWK31PxZL/bnNChZrxHWpaXgPH8ZXXAyaBiYT1r6ZOEaPweRM0I2L2RQ4mhEmETAoJgheE6aAUTHX\nXxOmyDYmM5hEZJuwtvX3B45h94eOJgHhbUwmhEm/5i8r5dSLv6d682ak242w23FNmULPG67Hkp4e\n+B1G/EbDf7kNf9mnPw/8m4j2uAaFFj1P/v/2zjzMjrrM95+3ztrd6aQ7IZCQBFnECIZVbEBEouCA\nPo7g3GFxxMsdVJjRGWXcRsc7XuTRGR14ZvDOON5xFMfl3hnQUcx1AQENInqJyBICEkFEQ0gIS9Lp\n9SxV7/2j6pyuc/qcPr2c7q7zy/t5nvPUb6vq37dred/6/d6qUqX8wgu8cMOXGL3nnlBDNkv3qQP0\nv/WtpHp7Ud8PnSM/AL+M+gHql0PHyy+HDlTZDx2rBsvQSfNbb8cPYm3q1i/7tXXRsloXBOj4OKVn\nn0VHRkAVyefpfd05HPKhD5FeuXLOMQczGTkAKAMB4V4ptWhrGEYCmO1dd+UCFBQKaKFQkw4i4x2m\nC2hhPCorRumorjCOVtOFqtGvpINiIayPbZt0Gi+XQ3I5Uv39+MPD6OhoeJH3PFLLl5M7+mgGN22q\nM9CZSQbb68ojS3uRTAbiBrxR+0x9fZgnM3m7kkrNeD/suvpq9t14E5LLocUiPa86c9FHQWbOEQwe\nvAktFqs60itX0nXccYvdsRkxdMstNRoya9bSG338qpOoOaYKBbyeJW27AZjJo4zvBd4J/CfhyMHX\nROTzqvqPbenJPOFSzMHGDjx46+kkHc3uuBdbg6qixSLB6Cg6OkpQ+Y2NRekxgtERdn/8mnBYOKI6\nfOp5LDnzTJ7fvZu+7u4JAz4+TlAsVg015TKSy1UNteTzsXQOL5cP6/M5JJcPy7JRu3wOWbo0LMvn\nkWyuJu3l67cXS9cZ3nqj2nvOOVWjutj7YiaUn3uevksuof/ii9h740018ROmY2FxQQNMrWOuzGTk\n4O3Aqao6AiAinwZ+BiTaOTCM2TLXeW5VDQ1txXCPjKJjdcZ8pGLUR9Aa415r8LUuH94VdyE93Xhd\n3Xjd3XhdXeGyuwvp7mbZBRcw9uCDFJ98EsplSKfJb9hA35svIL3yYHZsf5SXDAwguXzVWEs2Gxrw\nfD68S66f010E5vMCuJCs+6eJS+Xq//GxRezJ3HBBhwsaYH51zOQ9Bw8Br1DV8SifB36uqokeT7KY\nA2M6VAy5PzTEr88+pybQp0oqxUF/8ifTN+bj46GxrRrtbqQ7StcZ9Enl1Xxo6L34r6sLSU/Pr6/e\ndWezaLFI38UXd+BQtmEYM2UhYw6+BNwjIt+K8hcAX5ztHzbcZqGC4KpD7MPDBEND+MMjYXp4CH94\nmGB4hGB4iGB4OMwPDU+kh2vTksng9S4hfcgh+ENDBENDYfBYKkVmzRp6Tj8dgPSK5Xjr1iJdMYPe\nEzP0XV143T3hfPcs5qbbiSt33YZhLCwzfVrhZCaeVvgxcJaqXj9PfWsL69ev1+3bty92N+ZMJ82D\nAey6+uPsu/HGSXeqcR1aLOKPjMQM+4Qxr6aHhghGpjbsIoK3ZAnekiWkoqXX20tqSQ9eT5j2lvRE\ndbF0b2/YtifMSyYT63/zO+5O2xfNcEGHCxrAdCQJFzTAwo4coKr3AffF/vi3gUQ7B8b8oKro6Cj+\n/v3hb3AQf3CQnX/xvnB+OyL+DHH+2GNZsWcPvwoCguFh1PcnjHmdYY8b8/SqQyaMec+SsK5i2Jcs\nwctm267P7rgNwziQmdHIwaSVRXao6ro29qftdFrMwUI+k16dZ9+/H3/fIMH+wcjQ78ffHxr7YDAy\n/vtj6cFB/KEhJJ0mtXRp+Fu2DG/ZMiSbpfDooxR37AidhEyGrhOOZ/mlbyOzZk2NYZdcLhEBb4Zh\nGK6x0O85qGf2noXRkNlEyAfj4/iD+2uN+2Bk7OuN++Bg9W4/GBwMnxtftozUsqV4S5dVjb23LDT4\n2SOOiOqWkloatkstW4a3dGnTO/ZdV19N8cknq4+e5V58NEvPO7ed/ybDMAxjHmnpHIjIEI2dAAG6\n2t6jNtMp7zl49PgTat4EVx2OT6U46E//lN8+8jCH9vY2NPZAaMxjxj3Vt6xq7LOHrcNb+rKwvi8s\n8yp3+7lc27W48gxxM1zQAG7ocEEDmI4k4YKGdtDSOVDV3oXoiEuoahhgt3cv/t69lPfuxd+7L8zv\nq8tHP42G4CmXq2+CS69eTc+pp0LgEyxfQffLXx7euceMe2rp0vCZ9AQNz7vyDLFhGMaBypxiDjqB\n41es0PsefXTW8/eVwLtyzLg3NPpVw78Pf98+vHyeVH9/9Osj3ddfk0/195Puj5UtXcruT3zCnkk3\nDMMw5sxixxwknmB0tGb+Phgfx9+3r4WBry0jlWpg4EMjn3/p+jDdF3cE+pBZRNBbhLxhGIaRBJwf\nOdiQ79KvH354NS+ZDKnly0n19U0Y8xrjHivr7yfV14fXtfihFa7Mg7mgwwUN4IYOFzSA6UgSLmgA\nGzmYFpLN0nPmmRzyoQ+SOeywRM3PG4ZhGEbScH/koLtbv/6iw23+3jAMwzhgsJGDFuSOPJK+Cy+y\n+XvDMAzDmCbeYndgvhkulVj9Pz5W83hdJ7J58+bF7kJbcEGHCxrADR0uaADTkSRc0NAOnHcODMMw\nDMOYGc7HHHTatxUMwzAMY67MNebARg4MwzAMw6jBeeegU76t0ApX5sFc0OGCBnBDhwsawHQkCRc0\ntAPnnQPDMAzDMGaGxRwYhmEYhmNYzIFhGIZhGG3FeefAYg6ShQs6XNAAbuhwQQOYjiThgoZ24Lxz\nYBiGYRjGzLCYA8MwDMNwDIs5MAzDMAyjrTjvHFjMQbJwQYcLGsANHS5oANORJFzQ0A6cdw4MwzAM\nw5gZFnNgGIZhGI5hMQeGYRiGYbQV550DizlIFi7ocEEDuKHDBQ1gOpKECxragfPOgWEYhmEYM8Ni\nDgzDSCw3bLuBDSs2MLB6oFq2ZdcWtj2/jcs3XL6IPZsZpsNYaCzmwDCMhtyw7Qa27NpSU7Zl1xZu\n2HbDIvVo5mxYsYEP3PmBqo4tu7bwgTs/wIYVGxa5ZzPDdCQHF86LhcD5kYP169fr9u3bF7sbc2bz\n5s1s3LhxsbsxZzpBR6u7o07QABMX7uvOuo6B1QOT8tPRoar46uOrTzkoV3+VvB/4lLSEH/g15eWg\nTFnL1fKylqvtK+WloFSznUqb+u3sHN7J3U/fzZHLjuSJfU9wyqpTWJFfgaLs3r2blQevRFECDVAU\n1TAdEIBCQBCWRcuadGW9JulAA4Dq9lqtX6kLdOJvV7ZR8AuMlkbJeBlKQYnuTDe5VA6AcqlMLptD\nEESiH4In4f2bJ95EHTOrb1ReX4+Ah1fzt5vV7yvsY+uzWzms9zB2DO3gFatewaqeVaS9NLuf3s2L\n1r2IlJciLWnSXpqUpEh7telG9TVlXoqUpMh4GVKSCuu8NGlJV9PV7cbKKunK/2W+zosk0Oo6NdeR\ng3RbetkmROQ84DNACviCqn6qrv59wDuAMvAscLmq/nbBO2o4TeXuqNHFo92UgzJFv0gpKFHwCxT9\nIsWgGC7jv6is4BcoBaWG6frtlIIS63rXceXtV7KyayV7Rvewrncd1917HaWgxNDwEH/3zb+rMeA1\nxjoq88SbdAGuv5g3u8jXG4eGF/cGF/hcKlfdztretZSDMnftvIuz1p7FOS86p2q4Hh18lGPXHRsa\nsZjB8/BqDFrcSMaNYDVdWS9mbGvWabF+9e9Ff7vG+MbSX3zoi3z5kS9z2bGXcflxl1edirvvvpvT\nTj8NJbxZizs6NU4I2rC8Zp1W9TFHJr7duCPVqr4/18/tv7ud16x7Da9Z95oJx25PmYO7Dw4dvyB0\nKgt+gZHySEMHsnq81ZXFncdGjmPTYzZq54k3yXmIH28ZL8M7b3snfbk+BguDHLnsSP71oX/lK498\nhcEXBrnlrlvIelmyqejnZcmlcmRSGXKpXE1dLpWradd0nahMRNpy7Zjv61RiRg5EJAX8Cngd8BTw\nc+AtqvpIrM1rgHtUdVRE/hTYqKoXT7VdizlYWDptTtIPfMb9ccbKY4yXx8OfP859z9zH5x78HKcf\nejo/3flTLlx/IWuWrJlsvJulWxjySjrQoOlFpybd4MJTf3HKeJmG6Vt+cwvf/c13Of+o87lo/UXT\nNt7TuQtbCCoXvYvWX8RN22+qXgw7DdOxMFRGjqoOR2zUyle/6rh89ZGvctOvbuLNL34zbzrqTdVz\nuOqk153TBb9AMShS8kuT2hT8wkR5MPkaEHfY4+dmS4ei7jyvL396+Glufvxmfv+o3+cHT/6gZl+4\nNHIwADyuqk8AiMh/AOcDVedAVX8Ua///gEsXtIdGS9rpzZaC0oTBLo8z5ocGvGLIK/lqfXlsclnd\nOvWOQCkokU/n6Up30ZXuIp/Kk0+Hv758H7f99jaO6juKveN7GS2NhoY3OkF7Mj30p/qnNNz1J3wl\nXdlOWtJtu5NoxJZdW/jp0z/lyuOv5KbtN/Gmo96UqAt5K+qHfAdWDdTkOwXTsXCISDgVQYpsKtuw\nzZZdW7jtt7dVz4s3HvlGXrnmlfPet0CD2puEaTgUjZyQkdIIewt78dVnXe86btx+I1cef2Vb90GS\nnIM1wI5Y/ing1Cnavx34fqMKEbkCuAKgv7+/Zg6p8gxrp+UrZUnpz8aNG1FVbvvRbRS0wAmvOIGR\n0ghbH9zKGfkzeM+P3sMph5zCz3b+jFO6T+Hnz/ycu3bexT2/uIelK5bSt7KP8fI4u57bRTEokunJ\nMF4eZ3B0kFJQoiQlAg3IkiUjGZZ1LyOfzlMaLZGVLKsOWkU+nWfwuUGykuWow44in87z3FPPkZUs\nJ7z0BLrSXTz+6ONkJcupp5xKPpVn631byUqW1575WvLpPD/98U8RkUn6utd384E7P8B5y87jJ/t/\nwl8N/FV1PvKBBx7g3Ve9e8r/zxkbz1j0/bNl1xbee/t7+eOVf8wVJ13BwKqBifx5V0w6tha7v43y\nm7Zs4tK+S6sXvdHto1zadynbnt/GwOoBrr/+ek488cTE9LdZ/omDnuC6s65jdPsom7eH16PrzrqO\nTVs2MbpstLpOUvrr8v741fiv+Nq+r1X3R7YvW3VwRreP8sADD3DVVVfNy9//8Z0/ruZ76Z3W+j30\nNK3//C2fZ+fgzqqTk30my0vyL6nWz4UkTStcCJyrqu+I8m8DBlT1zxu0vRT4M+AsVS1Mtd1OCUhc\nqCC4clBmpDTCaGmUkdIII+WRcBn7VetKI4yWRyeXlyfyKUnRk+mhO9NNT6anmt41vIsnBp/gZSte\nxsCqgerd+I4ndnD8MceHd+lRWT6Vr+ary1QXaW9+76qbcaAELHWKjqlwQQOYjoXElfOi1XVqrtMK\nSXIOTgeuVtVzo/xHAFT1b+vanQP8I6FjsKfVdjsl5qDZjr72rGs57qDjJox3udZ4V4z0cGm4uTGP\nlZeCEj3pyca8J90zycj3ZHroTtfma+oy3WS8TFMtSZ2TbEWnxU0YhnHgMd9PKyTJOUgTBiSeDewk\nDEj8I1V9ONbmJOAbwHmq+th0tltxDhbj4q6qFPwCQ8UhhopD7C/ur6aHikMMlWrLnhp6ikdfeJQl\nmSXsL+4nl8pRDIpkvWzVaC/JLJkw4OkGxnwKQ9+T6SGfys/7HPdU3qxhGIYx/zgTkKiqZRH5M+BW\nwkcZb1DVh0XkGuBeVd0EXAssAb4eGbjfqeqbptru0NDQrIPiVJVxf7zGoE8y8E2MfiUtCL3ZXnqz\nvSzNLq2m42Vrl6yt5m998lZufvxm3nrMW3nXie+iO91N2kt3zFDXtue31TgCA6sHuO6s66pzkp2i\nYypc0ABu6HBBA5iOJOGChnaQGOcAQFW/B3yvruxjsfQ5M93mPn8f79v8Pt5/yvvJpDL8+KkfT8/A\nR7+Ul5ps0DMTRn5Zbhlre9c2NPy92d7qS06mw5ZdW7hzx53V4JLXrnttx91tNxqZGVg90HE6DMMw\nDmQSM60wX3Qd0aXHf+J4VvWsmnTHPpXRr/yaPQrTbmw43jAMw2gXzkwrzBcru1aSS+X46KkfTbSR\nbTUcbxiGYRgLhfMfXuryu7jurOtqPhaSRC7fcPkkJ2Bg9UB1mD7+THon44IOFzSAGzpc0ACmI0m4\noKEdOO8cQO1duGEYhmEYU+N8zEGnvOfAMAzDMNrFXGMODoiRA8MwDMMwpo/zzsHQ0NBid6EtuDIP\n5oIOFzSAGzpc0ACmI0m4oKEdOO8cGIZhGIYxMyzmwDAMwzAcw2IODMMwDMNoK847BxZzkCxc0OGC\nBnBDhwsawHQkCRc0tAPnnQPDMAzDMGaGxRwYhmEYhmNYzIFhGIZhGG3FeefAYg6ShQs6XNAAbuhw\nQQOYjiThgoZ24LxzYBiGYRjGzLCYA8MwDMNwDIs5MAzDMAyjrTjvHFjMQbJwQYcLGsANHS5oANOR\nJFzQ0A6cdw4MwzAMw5gZFnNgGIZhGI5hMQeGYRiGYbQV550DizlIFi7ocEEDuKHDBQ1gOpKECxra\ngfPOgWEYhmEYM8NiDgzDVa49Gkb2TC7vORg++NjC92c2uKDBMBaBucYcpNvZGcNwAlcMUiMNU5Un\nERc0gDvHlHHA4Lxz4FLMwcaNGxe7G61pcRHsCB0tDNK8a1AFDSAoQ+CD+lE6iKUr5X5dm1j5VDx+\nBw9ufZATjj8eqoOHOvH3FyVP4/pmPPJttj38CBuOOx4kBeKB54VL8SbKxAOvkpZY21RdW2lS7tVt\nu9F2o203Y7GPqXbhwvndAhc0tAPnnQNjgVmoOz1V8EvgF+t+pdp0udC43C+CX2hcPhU3XsqGPXtg\n5z/HjHM5Mub1RnqWBl6DCSPkpcBLR2kvlo5+U7WZip/+I+v27oWRu6KCyLBVDdxc88ywfZP8VDz0\ndVbt2QOlB8P/mUb/8+q+0CblQe2vpiy23qTyINp/weTtahD2uaHT0GJffO4MXj4yBo+vgFQm3H+p\nDHiZcFlNpyGVnSivtEtlp1in0i47t3Uq+8WFkRwbxZkWFnOQFJJ+wKpCeRxKY3XLcSiPTSxvvLT5\nNl755zGD3chYNzP00TK+XlCKLmTZiYtdOjeRri4r6VyT8iyks7Vld1zTXMOFX25hsKcq9+raNDPw\nqanvQqfL1cumqBuc+/YXgk7RoDrZGYk7F59+UfN1r7wrPJ798sSx7ZejZTGWLk0sq+lG69S1bbRO\no23XrFOeONfUD49NLxOe48044tWQzofnYXXZFctHZZmuujaN2kbLTLReKheeU+2gU46pOWIxB64w\nU488CGqN8rSXYw0MfBNDX7McDw1nuis8uTP5KF23nIqelXUGOtfYWDcy2JMMfaY9BrQRUzkHL7tg\nfv6m0dmIRMejx4wvq6uPn48etY+gMgJWgr85tHm7M98fOvDVa8n4RL5cCK8l4/uifFRWGqtrE2tb\nX57KTs+RaOVwTMXe30J2CWS7w7bzdY1pB/N8Q+m+c/D0/ROe4kLehauGB35xBEoj4bI4AsXhxump\n+OxpjA3tpSvNhLH2CxPeeCYy2JOMdVRXv+zqn2KdZst8eEfbiqm88jPe68R8Xsdo6Dm4+cWDDtHh\ngoZpkGgdngdeFshO3e7IjZGOc9vfB9XpOxFVp6OubHQoXE7Fl95AaXSQjEajMZluyPZMXlbT3aEz\nUUlnorpqurJeXTrd4n85HeZ5isd95yBOs3+aX25hwEcblE9h6CsOQSo7cSBllzROZ6KDayr+8Its\n/cVWTn3VWbWGPslebSfTwiB1DEmYjporLmgAd46pxUIkvEnJtLjznw5bPt+87n0Pc3fFUavahVEo\njdZe26tlwxPp0ReguGOibc06oxN2pBTdDNY4EvVOxzQckXnG/ZiDQ1N67xUx47v2FZONuV9sbLAb\nGvYpDH38l+kJA4imiyvzYEmPnTAMY/a4cH4n4VpbLoY2qOI0VG9ORyecj6kckeIIPPGjKXVYzMFM\nOfdvJht2uwtvH51ygTAMY+a4cH4nYRQnnYX0cmD57LcxlZPTBg681yevG4BDXgb9h0PPQeEQfRIc\ng2YHZmxu1QVc0OGCBnBDhwsawHQsKB98LBwhqP9Fjk9HaFgADryRg6TigkduGIZhLAzzPAJyYMUc\ndNK8mGEYhmHMEos5aMWhJ8HVHfASJMMwDMNICM7HHLj0bQUXcEGHCxrADR0uaADTkSRc0NAOnHcO\nDMMwDMOYGe7HHHTKtxUMwzAMo03MNebARg4MwzAMw6jBeefAYg6ShQs6XNAAbuhwQQOYjiThgoZ2\n4LxzYBiGYRjGzLCYA8MwDMNwDKdiDkTkPBHZLiKPi8iHG9TnROTGqP4eETl84XtpGIZhGG6TGOdA\nRFLAZ4HXA8cCbxGRY+uavR3Yq6ovBv4B+HSr7VrMQbJwQYcLGsANHS5oANORJFzQ0A4S4xwAA8Dj\nqvqEqhaB/wDO7BnFUQAAFC9JREFUr2tzPvDlKP0N4GyRJHw1yTAMwzDcIUnOwRpgRyz/VFTWsI2q\nloFBYEX9hkTkChG5V0TuHRwcrPEEN2/e3JH5jRs3Jqo/s81XypLSn9nk67Usdn9mm9+4cWOi+jOb\nfKUsKf2Zbd7O7+Tk67Usdn/muj9mS2ICEkXkQuBcVX1HlH8bMKCqfx5r83DU5qko/+uozfPNtmsB\niYZhGMaBhksBiU8B62L5tcDTzdqISBpYBrww1UYt5iBZuKDDBQ3ghg4XNIDpSBIuaGgHSXIOfg4c\nLSJHiEgWuATYVNdmE3BZlP5D4IealKEPwzAMw3CExEwrAIjIG4DrgRRwg6p+UkSuAe5V1U0ikge+\nCpxEOGJwiao+MdU2bVrBMAzDONCY67RCup2dmSuq+j3ge3VlH4ulx4ELF7pfhmEYhnEgkaRphXnB\nYg6ShQs6XNAAbuhwQQOYjiThgoZ24LxzYBiGYRjGzEhUzMF8YDEHhmEYxoGGS48yGoZhGIaRAJx3\nDizmYOG5+f6dnPGpH3LEh7/LGZ/6ITffv7Na1yk6XNAAbuhwQUMrTEdycEFDO0jU0woHOjffv5Nr\nb93O0/vGOLSviw+eu54LTqp/g3Syufn+nXzkmw8xVvIB2LlvjI988yGAjtHiggZwQ4cLGgyjE7GY\ng4RQfxEE6Mqk+Ns/OG5RL4KqSslXykEQLv1wWfIDykGYL/oB5ajNlV/9Bc8NFydtZ3l3hmsu2ECg\nEARKoDop7auiqgSB4mv4twNV/ACCqK4mXdlGtE7DtCpBtE64/Sgd1KaD6O/5qvzs189TKAeTNOTS\nHi9/UT+VT30JYaL+01+Vb4FJNU9dfmKF+jbUbXPyNurq68rjizt++Qxjpck6urMpLjhpDSkRUl7d\nTwQvWqZTgidCygNPhHTUplIfX69SX61LTbTxarZVv25l2x6eR00/Up7w+s/cxa7B8Uka1vR1cfeH\nXzupPMm44PwbnYNT7zmYDx7aOcgZn/phYk5EVaVQDqKfT6EULj/5vV/WOAYAYyWfj//fhwlUKfsa\nGeHQKFcNtB9QCuqMdrScVD6FgY/nS35Q3U45UNJeeHHPeB6ZtEfaEzIpLyxL1eYbOQYAL4yW+P5D\nuxGhaiSqaRE8LzSaKRE8idJemPYig1VNi5DyPDJCVB7WhdtskI7Wkai8Ph3frufB5u3PNtRQKAe8\na+OLw/2IRvsz2q+x/RvPU62vbT+ddbVuI5PbN952pf47W3c11DFa9Dlm9dLQCav8NJYOQkevUK6t\nD4LweAiqbcEPAvzIySsHQdV584NW6zaoq/Yj2m6g7B8vN9Swc98YJ13zAzIpj0zKI5v2yETHYybl\nkU15ZNJ1+Up9ujYfrhsrq8lPlNXkY+tlUjJRl57Ix51Al0ZAzMk5MHDeOYDJJ2LZD6oGerzk1xnq\n2nRNfTmoGvN4u/FyQGGKduOxdMkPyKY8cmmPXCYVLtMezw4VGvZ972iJux57juf2PMO6tYeS8YR0\nZIizKY+055HLSPWClU551TaZOuM9YdDDO7XKhS1d02Zi3bCNMJOvYp/xqR+yc9/YpPI1fV189q0n\nszn2Bbqksqavq6mGVx19UEdoAPj09x9tquNtp72oI3Q0O55WL8vz3fecyZ13/YRTBk6LHNrQsS36\nAaVyXb7yKyuFan0Q1SvDhXK1rOhPOMglP6BYrsv7Wrt+efI6JV9rnJWh8RJB3SDtWMnnL/9zK9/Z\n+jT79z7PukNXk8945NIp8hmPfCa2TKfITaqL0lFdPh2W5dIenjc/X7Jv5eR0wjHVChc0tIMDwjmA\n8ET8ixsf4P1ffxBVrZ5E+aqBrpx8UTpel5koy6U9enJplvfUGvf4+jXbbLB+I2M7lVH9h4tPjA7Y\n4xbiXzUnPnju+obTIx88d/0i9mpmuKAB3NDRTMNfnvdSlvdk6c97rFvevYg9bExlOq7iLJx0zW0N\n2xXKARedso77tg5y5JHLKZT86s3EeClg/1iJ8ShduVEZL1VuSnzGS5Prin5AJuWRr1yLYo5DxbHI\n1TgUXo2jEV8nF3dE0h6f/G7jEc5rb91uoweO4XzMQW710br6suuBcB72sU++nnQqeQ9pJDXmYDa4\nMOzoggZwQ4cLGqZy/tsdOxEE4RRkI8dhvOQzHksXYo5IWNdonUobn58/ubfp3z1oSZaubIqebJru\nbIruaNmTq+TDsp7cRF19viebpjsXLvOZxjdS7cCFY6oVc405OKCcg6QHMR0IB6xhHIi44vxPNc2z\n6c9exWixzEjBZ7RYZrToT8qPFH1GC2VGS+FypBhrW/AZia1XKAd0Z1J052KORDbKZ1JVJ6I7l6I7\nU+90xJ2SWifl+w/t4q++ta3j90UrLCBxmnTCcOoFJ61penC6Mg/mgg4XNIAbOjpFQ+W8bub8d4qO\nqaZ5Vvbm2Lz5Z23T4QcaczJ8RgoTjkNtPix7el+pqVMSOiZhutjgSaSxks+Hv7mVux9/jn3P7eaY\nFx/B0nyaJbk0vfkMvfk0S/JpluYn8l2Z1LyNbEyX+byhPCCcgzV2F24YxiIzlfPfKbRyctpJypPI\nEGfaut0jPvxdGo2Xj5cCTjm8n/uH9yDA0/vGGRovMTReZrhQrqaHonTJ18h5CJ2IpTEnojdyIsLy\nmIMRczYq+dlOc8/3EzDOTyt0ynsODMMwjPmnXfEfJT9geLzM0HiZ/eOlyIEo1zgU+yvpuvLKOiOF\nMvlMqupkxB2H3lzc2YjKY87FO79yL3saPOVW0WHTCoZhGIYxTdr1FE8m5dHfk6W/JzvrvgSBMlry\nJ0YlJjkRYXrHC6PVuopz0cgxAHi6geMzG5x3Dlz6tkInzEm2wgUdLmgAN3S4oAFMx0KSpPgPzxOW\n5MLphdXLZrZusxGQQ/u62tI3550DwzAMw4jjQvzHfL/HxGIODMMwDKMDmeppBYs5MAzDMIwDkPkc\nAUneqwLbjEsxBy7ggg4XNIAbOlzQAKYjSbigoR047xwYhmEYhjEzLObAMAzDMBxjrjEHNnJgGIZh\nGEYNzjsHFnOQLFzQ4YIGcEOHCxrAdCQJFzS0A+edA8MwDMMwZobFHBiGYRiGY1jMgWEYhmEYbcV5\n58BiDpKFCzpc0ABu6HBBA5iOJOGChnbgvHNgGIZhGMbMsJgDwzAMw3AMizkwDMMwDKOtOO8cWMxB\nsnBBhwsawA0dLmgA05EkXNDQDpx3DgzDMAzDmBkWc2AYhmEYjmExB4ZhGIZhtBXnnQOLOUgWLuhw\nQQO4ocMFDWA6koQLGtqB886BYRiGYRgzw2IODMMwDMMxLObAMAzDMIy24rxzYDEHycIFHS5oADd0\nuKABTEeScEFDO3DeOTAMwzAMY2ZYzIFhGIZhOIbFHBiGYRiG0Vacdw4s5iBZuKDDBQ3ghg4XNIDp\nSBIuaGgHiXAORGS5iNwmIo9Fy/4GbU4UkZ+JyMMislVELl6MvhqGYRiG6yQi5kBE/g54QVU/JSIf\nBvpV9S/r2rwEUFV9TEQOBX4BHKOq+6batsUcGIZhGAcarsQcnA98OUp/GbigvoGq/kpVH4vSTwN7\ngJUL1kPDMAzDOEBIinNwiKruAoiWB0/VWEQGgCzw61YbtpiDZOGCDhc0gBs6XNAApiNJuKChHaQX\n6g+JyO3AqgZVH53hdlYDXwUuU9WgSZsrgCuibEFEts3kbySUg4DnFrsTbcAFHS5oADd0uKABTEeS\ncEEDwPq5rJyUmIPtwEZV3RUZ/82qOkmYiCwFNgN/q6pfn+a2753LvEtSMB3JwQUN4IYOFzSA6UgS\nLmiAuetIyrTCJuCyKH0Z8O36BiKSBb4FfGW6joFhGIZhGDMnKc7Bp4DXichjwOuiPCJyioh8IWpz\nEfBq4L+JyAPR78TF6a5hGIZhuMuCxRxMhao+D5zdoPxe4B1R+mvA12ax+c/PrXeJwXQkBxc0gBs6\nXNAApiNJuKAB5qgjETEHhmEYhmEkh6RMKxiGYRiGkRCccg5EZJ2I/EhEfhm9Zvm9UXnL1zMnCRHJ\ni8gWEXkw0vHxqPwIEbkn0nFjFKSZaEQkJSL3i8h3onwnanhSRB6K4lzujco67ZjqE5FviMij0flx\negdqWB+LN3pARPaLyFUdqOMvovN6m4j8e3S+d+J58d5Iw8MiclVUlvh9ISI3iMie+CPuzfotIf9T\nRB6PXtt/8uL1fIImGi6M9kUgIqfUtf9IpGG7iJw7nb/hlHMAlIH3q+oxwGnAu0XkWODDwB2qejRw\nR5RPMgXgtap6AnAicJ6InAZ8GviHSMde4O2L2Mfp8l7gl7F8J2oAeI2qnhh7NKjTjqnPALeo6kuB\nEwj3SUdpUNXt0T44EXg5MEr4BFPH6BCRNcB7gFNUdQOQAi6hw84LEdkAvBMYIDye3igiR9MZ++Lf\ngPPqypr1+/XA0dHvCuBzC9THVvwbkzVsA/4A+HG8MLKBlwAvi9b5ZxFJtfwLqursj/CRyNcB24HV\nUdlqYPti920GGrqB+4BTCV/MkY7KTwduXez+tej7WsIT7bXAdwDpNA1RP58EDqor65hjClgK/IYo\nxqgTNTTQ9HvA3Z2mA1gD7ACWEwaEfwc4t9POC+BC4Aux/F8DH+qUfQEcDmyL5Rv2G/gX4C2N2i32\nr15DrHwzofNZyX8E+EgsfytweqvtuzZyUEVEDgdOAu5hhq9nTgLRcPwDhN+QuI3wVdH7VLUcNXmK\n8EKTZK4nvGBU3mS5gs7TAKDAD0TkFxK+fRM665g6EngW+FI0xfMFEemhszTUcwnw71G6Y3So6k7g\nOuB3wC5gkPAjcp12XmwDXi0iK0SkG3gDsI4O2hd1NOt3xZmr0An7pp5ZaXDSORCRJcB/Alep6v7F\n7s9sUFVfw+HTtYRDd8c0arawvZo+IvJGYI+q/iJe3KBpYjXEOENVTyYcYny3iLx6sTs0Q9LAycDn\nVPUkYIRkDvdOi2g+/k1Ax70MLZrLPh84AjgU6CE8rupJ9Hmhqr8knAq5DbgFeJBwWtc1OvWaFWdW\nGpxzDkQkQ+gY/G9V/WZU/IyEr2WufJthz2L1b6Zo+EnqzYQxFH0iUnk3xVrg6cXq1zQ4A3iTiDwJ\n/Afh1ML1dJYGoPoVUFR1D+Ec9wCddUw9BTylqvdE+W8QOgudpCHO64H7VPWZKN9JOs4BfqOqz6pq\nCfgm8Eo687z4oqqerKqvBl4AHqOz9kWcZv1+inBEpEJH7Js6ZqXBKedARAT4IvBLVf37WFXL1zMn\nCRFZKSJ9UbqL8ILyS+BHwB9GzRKtQ1U/oqprVfVwwiHgH6rqW+kgDQAi0iMivZU04Vz3NjromFLV\n3cAOEal8r+Rs4BE6SEMdb2FiSgE6S8fvgNNEpDu6XlX2RUedFwAicnC0PIwwEO7f6ax9EadZvzcB\n/zV6auE0YLAy/dBBbAIuEZGciBxBGFy5peVaix1U0eYAjVcRDpdsBR6Ifm8gnOu+g9CzvQNYvth9\nbaHjeOD+SMc24GNR+ZHRTn2ccEg1t9h9naaejcB3OlFD1N8Ho9/DwEej8k47pk4E7o2OqZuB/k7T\nEOnoBp4HlsXKOkoH8HHg0ejc/iqQ67TzItJxF6Fj8yBwdqfsC0InZhdQIryrfnuzfhMOyX+WMObr\nIWKBfgnU8OYoXQCeIRbUSvj1418TBlS+fjp/w96QaBiGYRhGDU5NKxiGYRiGMXfMOTAMwzAMowZz\nDgzDMAzDqMGcA8MwDMMwajDnwDAMwzCMGsw5MIwOJHptbeXrhLtFZGcsn8gv+onI5SKyap62/WIR\nGZPoq5lR2aEiclP0NbpHROS7UbvK1x33zUdfDMMF0q2bGIaRNFT1ecJ3FyAiVwPDqnrdonYq7EtK\nVf0m1ZcTfkRs9wy2l9aJbw60YrtGX82MXjB0M/B5Vb0oKjuZ8B36d0eftH1uuv0wjAMNGzkwDMcQ\nkctEZEt0d/zPIuKJSFpE9onItSJyn4jcKiKnisidIvKEiLwhWvcdIvKtqH67iPz3aW73EyKyBRgQ\nkY+LyM9FZJuI/K/o7XIXEzozN1ZGN0TkqdibQE8Tkduj9CdE5F9E5DbCj0WlReTvo7+9VUTeMY1/\nw+sIHaYvVApU9T5Vvbt9/2nDcBdzDgzDIURkA+Gb0l6p4Ye70oSvrwZYBvxAw49IFYGrCV/feyFw\nTWwzA9E6JwN/JCInTmO796nqgKr+DPiMqr4COC6qO09VbyR8Y+nFqnqiqhZbSDkJ+H1VfRtwBeFH\nvAaAVxB+/OqwFutvIPzaoWEYs8CmFQzDLc4hNKD3hiPrdDHxudYxVb0tSj9E+J74sog8RPht+Aq3\nqupeABG5mfC15Okptlsk/CBVhbNF5INAHjiI0Eh/f4Y6vq2q41H694BjRCTujBxN+J0CwzDmAXMO\nDMMtBLhBVf+6pjD84l/8bj0gfAd7JR2/FtS/U11bbHdMKy+iF+kG/gk4WVV3isgnCJ2ERpSZGL2s\nbzNSp+ldqnpHk+004mHgjTNobxhGDJtWMAy3uB24SEQOgupTDa2G4Ov5PRHpiwz9+cDdM9huF6Gz\n8Vz0Ncv/EqsbAnpj+SeBl0fpeLt6bgXeFTkiRE8bdLXQ8ANgqYhcXimIYizObLGeYRjYyIFhOIWq\nPiQiHwduFxGP8Kttf8LMvkH/E+D/AEcBX1XVBwCms11VfV5Evkz4xcHfAvfEqr8EfEFExgjjGq4G\n/lVEdjP1J2T/BTgMeCCa0thD6LQ0RVVVRM4HPiMiHwXGgN8AV7VUbxiGfZXRMIwJoicBNqhqRxlR\nEXkx8I0oWHI67dPAc6raN789M4zOxKYVDMNwgTKwIv4SpGaIyHrgXsJv3huG0QAbOTAMwzAMowYb\nOTAMwzAMowZzDgzDMAzDqMGcA8MwDMMwajDnwDAMwzCMGsw5MAzDMAyjBnMODMMwDMOo4f8Dr/jW\nWXedJmsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1, figsize=[8, 6])\n",
    "plt.plot(temp[0::10], si_halite_bc[0::10], marker='*', linestyle='-.', color ='k', label=(\"%sM%s\" % (NaCl, 'NaCl')))\n",
    "\n",
    "plt.figure(2, figsize=[8, 6])\n",
    "plt.plot(temp[0::10], log_mean_gam_nacl_bc[0::10], marker='*', linestyle='-.', color ='k', label=(\"%sM%s\" % (NaCl, 'NaCl')))\n",
    "    \n",
    "for i in range(0, np.size(log_mean_gam_salt, 1)):\n",
    "    plt.figure(1, figsize=[8, 6])\n",
    "#     plt.plot(temp[0::10], si_halite_bc[0::10], marker='*', linestyle='-.', color ='k', label=(\"%sM%s\" % (NaCl, 'NaCl')))\n",
    "    plt.plot(temp[0::10, i], si_h[0::10, i], marker=mm[i], label= (\"+%sM%s\" % (X[i], added_salt)))\n",
    "\n",
    "    plt.figure(2, figsize=[8, 6])\n",
    "#     plt.plot(temp[0::10], log_mean_gam_nacl_bc[0::10], marker='*', linestyle='-.', color ='k', label=(\"%sM%s\" % (NaCl, 'NaCl')))\n",
    "    plt.plot(temp[0::10, i], log_mean_gam_nacl[0::10, i], marker=mm[i], label=(\"+%sM%s\" % (X[i], added_salt)))\n",
    "\n",
    "    plt.figure(3, figsize=[8, 6])\n",
    "    plt.plot(temp[0::10, i], log_mean_gam_salt[0::10, i], marker=mm[i], label=(\"+%sM%s\" % (X[i], added_salt)))\n",
    "\n",
    "plt.figure(1)\n",
    "plt.xlabel('Temperature [C]')\n",
    "plt.ylabel('Halite SI')\n",
    "plt.xlim([0, 120])\n",
    "plt.legend()\n",
    "# plt.ylim([-0.06, 0.10])\n",
    "plt.grid()\n",
    "plt.figure(2)\n",
    "plt.xlabel('Temperature [C]')\n",
    "plt.ylabel(r'Log $\\gamma_{NaCl}$')\n",
    "plt.xlim([0, 120])\n",
    "plt.legend()\n",
    "# plt.ylim([-0.08, 0.04])\n",
    "plt.grid()\n",
    "plt.figure(3)\n",
    "plt.xlabel('Temperature [C]')\n",
    "plt.ylabel(r'Log $\\gamma_{%s}$' % added_salt)\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}