boxmodel.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model runs, data, and analysis routines for exploring the \"Ligand-Iron-Microbe\" feedback (Lauderdale, Braakman, Forget, Dutkiewicz, and Follows)\n",
    "\n",
    "<img src=\"ligand_feedback.png\" width=\"350\" />\n",
    "\n",
    "Given the organic origin of iron-binding ligands, we hypothesize that a positive feedback between microbial activity, ligand abundance, and iron availability could emerge. \n",
    "Iron is supplied by dust deposition, sediment mobilization, and hydrothermal activity. \n",
    "In a hypothetical watermass where ligand abundance is initially very low, Fe(III) is largely insoluble, but a small population of microbes subsist. Their turnover produces ligands such as siderophores, excreted organic carbon, or chelating detritus. Greater ligand abundance retains more iron in solution, incrementally relieving iron limitation, promoting further biological production, and so on.\n",
    "Eventually, other requirements such as macronutrients, become limiting, and additional iron no longer increases productivity. At appropriate time and space scales, the global ligand pool is regulated, supporting \"just enough\" iron to match availability of other resources redistributed by ocean circulation, maximizing overall nutrient consumption and global productivity. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import cmocean           as cm\n",
    "import glob              as gb\n",
    "import matplotlib        as mp\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy             as np\n",
    "import numpy.ma          as nm\n",
    "import pandas            as pd\n",
    "import warnings          as ws\n",
    "from matplotlib.ticker     import FormatStrFormatter\n",
    "from itertools             import zip_longest\n",
    "\n",
    "import utils\n",
    "\n",
    "# Output figures as pdf or eps\n",
    "figfmt='pdf'\n",
    "#figfmt='eps'\n",
    "\n",
    "# Input/output files\n",
    "finname ='boxmodel_parain.csv'\n",
    "foutname='boxmodel_paraout.csv'\n",
    "\n",
    "# Some figure options for later\n",
    "mp.rcParams['xtick.labelsize'] = 14\n",
    "mp.rcParams['ytick.labelsize'] = 14 \n",
    "\n",
    "len_xaxis,len_yaxis = 4,4 #fix here your numbers\n",
    "xspace, yspace = .9, .9 # change the size of the void border here.\n",
    "x_fig,y_fig = len_xaxis / xspace, len_yaxis / yspace"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Set to True, the script will call the box model nincs^2 times (first, must have compiled boxmodel.f with f2py;  an ensemble of integrations could take a long time), set to False and it will load previously run results from the paper and plot them**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "RUNMODEL=True\n",
    "PARALLEL=True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Model parameters and initial conditions\n",
    "\n",
    "<img src=\"boxmodel_schematic.png\" width=\"350\" />\n",
    "\n",
    "The box model has three boxes linked by an overturning circulation: an upwelling box with low iron input analagous to HNLC regions like the Southern Ocean, and a deep water formation region with significant iron input analagous to the Atlantic Ocean. Iron-binding ligands are produced by organic matter turnover, and lost by microbial degradation.\n",
    "\n",
    "Set a few parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of increments in parameter space\n",
    "nincs=100\n",
    "\n",
    "# Fixed value for uniform ligand control experiment\n",
    "fixedligconc=1.0\n",
    "\n",
    "# Do the analysis using nitrate (NP=16) or native phosphate (NP=1)\n",
    "R_np=16\n",
    "R_cp=117\n",
    "\n",
    "# Convert mol/kg to mol/m3\n",
    "conv = 1024.5 \n",
    "         \n",
    "# Total number of model runs in ensemble\n",
    "niters=nincs*nincs\n",
    "\n",
    "# Start counting the model runs at ninit\n",
    "ninit =0\n",
    "\n",
    "# Box dimensions (m)\n",
    "dx=np.array([17.0e6, 17.0e6, 17.0e6]) \n",
    "dy=np.array([ 3e6,   13e6, 16.0e6]) \n",
    "dz=np.array([50.0,   50.0, 5050.0]) \n",
    "\n",
    "area = dx * dy \n",
    "vol= area * dz \n",
    "invol = 1.0 / vol\n",
    "\n",
    "psi=20.0e6 # Sv\n",
    "\n",
    "# Biological production maximum rate per year\n",
    "alpha_yr=6e-6\n",
    "\n",
    "# Deep ocean box lifetime modifier - capture the gradient introduced by photodegradation near the surface and slower loss in the deep ocean\n",
    "dlambdadz=0.01"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Surface iron input rate (Atlantic receives 1.00xdep [g/m2/yr] while SO receives 0.01xdep)**\n",
    "\n",
    "Default value in the model (7.0) is taken from the box model paper (Table 1) in Parekh et al (2004) with a asymetry value of 0.01. Data from Mahowald et al (2006) suggests:\\\n",
    "• Whole NA: 0.25 gFE m-2 yr-1\\\n",
    "• 0 to 20N: 0.40 gFE m-2 yr-1\\\n",
    "• 20N ++  : 0.16 gFE m-2 yr-1\\\n",
    "• 40N ++  : 0.10 gFE m-2 yr-1\\\n",
    "• Whole SO: 0.0015 gFE m-2 yr-1\\\n",
    "• Whole SH: 0.0033 gFE m-2 yr-1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dust deposition in g Fe m-2 year-1\n",
    "dustdep=0.15\n",
    "# Hydrothermal vent input of 1 Gmol/yr (Tagliabue et al., 2010)\n",
    "# mol Fe/yr * g/mol * 1/area  == g Fe m-2 year-1....\n",
    "# divide by 2.5e-3 because fe_sol=2.5e-3 is multiplied again within the box model.\n",
    "ventdep=(1e9*56)/(area[2]*0.0025)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Get reference values for model-data comparison**\n",
    "\n",
    "If World Ocean Atlas or GEOTRACES IDP files are not present, then the values given in Table 1 of the paper are used instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nitrate reference values are:  [23.974  2.958 31.621]  mmol/m3.\n",
      "Nitrate st. deviation values are:  [ 3.688  5.073 11.495]  mmol/m3.\n",
      "Total Iron reference values are:  [0.249 0.288 0.69 ]  umol/m3.\n",
      "Total Iron st. deviation values are:  [0.439 0.347 2.311]  umol/m3.\n",
      "Total Ligand reference values are:  [1.872 1.972 2.374]  umol/m3.\n",
      "Total Ligand st. deviation values are:  [1.006 0.896 3.364]  umol/m3.\n"
     ]
    }
   ],
   "source": [
    "# Print out objective function reference values\n",
    "PRINTREF=True\n",
    "\n",
    "if R_np==16:\n",
    "    woafile='woa13_annual_nitrate.nc'\n",
    "else:\n",
    "    woafile='woa13_annual_phosphate.nc'\n",
    "\n",
    "nref, nstd = utils.get_macro_reference(woafile,Rnp=R_np)\n",
    "    \n",
    "idpfile='GEOTRACES_IDP2017_v2_Discrete_Sample_Data.nc'\n",
    "\n",
    "fref, fstd, lref, lstd = utils.get_micro_reference(idpfile)\n",
    "\n",
    "pd_refs=pd.DataFrame(data=np.concatenate((\n",
    "                    nref,nstd,fref,fstd,lref,lstd),axis=1),\n",
    "                    columns=[\"nsoref\",\"nnaref\",\"ndoref\",\"nsostd\",\"nnastd\",\"ndostd\",\n",
    "                             \"fsoref\",\"fnaref\",\"fdoref\",\"fsostd\",\"fnastd\",\"fdostd\",\n",
    "                             \"lsoref\",\"lnaref\",\"ldoref\",\"lsostd\",\"lnastd\",\"ldostd\"]\n",
    "                    )\n",
    "if PRINTREF:   \n",
    "    # Print out reference values\n",
    "    if R_np==16:\n",
    "        print('Nitrate reference values are: ',np.str(np.round(*nref,3)),' mmol/m3.')\n",
    "        print('Nitrate st. deviation values are: ',np.str(np.round(*nstd,3)),' mmol/m3.')\n",
    "    else:\n",
    "        print('Phosphate reference values are: ',np.str(np.round(*nref,3)),' mmol/m3.')\n",
    "        print('Phosphate st. deviation values are: ',np.str(np.round(*nstd,3)),' mmol/m3.')\n",
    "    print('Total Iron reference values are: ',np.str(np.round(*fref,3)),' umol/m3.')\n",
    "    print('Total Iron st. deviation values are: ',np.str(np.round(*fstd,3)),' umol/m3.')\n",
    "    print('Total Ligand reference values are: ',np.str(np.round(*lref,3)),' umol/m3.')\n",
    "    print('Total Ligand st. deviation values are: ',np.str(np.round(*lstd,3)),' umol/m3.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. TIMESERIES MODEL OUTPUT: Model illustration using single (reasonable) strength of the ligand-iron-microbe feedback (Figure 3 in our paper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 480x880 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import the box model to the main script to run an illustrative simulation\n",
    "import importlib.util as iu\n",
    "spec = iu.spec_from_file_location(\"nutboxmod\",gb.glob(\"nutboxmod*.so\")[0])\n",
    "nutboxmod = iu.module_from_spec(spec)\n",
    "spec.loader.exec_module(nutboxmod)\n",
    "\n",
    "example_gaovla = 4500.\n",
    "\n",
    "#gamma input to the model is in phosphate units, not carbon units...multiply by RCP=106 to get from values referenced in paper \n",
    "# (also applies to gamma_over_lambda...lambda should be in 1/seconds)\n",
    "example_gamma  = (5e-5*R_cp)\n",
    "example_lambda = 1./((example_gamma/R_cp)/example_gaovla)\n",
    "\n",
    "pexample=np.tile(33/R_np,(1,3))\n",
    "fexample=np.tile(1e-6,(1,3))\n",
    "lexample=np.tile(1e-6,(1,3))\n",
    "\n",
    "# Create a consolidated Pandas dataframe with the input values\n",
    "example_timein=pd.DataFrame(data=np.concatenate((np.array((1e4,)),np.array((1,))),axis=0)[np.newaxis,:],\n",
    "                    columns=[\"nyrs\",\"tout\"])\n",
    "\n",
    "example_datain=pd.DataFrame(data=np.concatenate((\n",
    "                  pexample*1.0e-6,fexample*1.0e-9,lexample*1.0e-9),axis=1),\n",
    "                  columns=[\"nso\",\"nna\",\"ndo\",\"fso\",\"fna\",\"fdo\",\"lso\",\"lna\",\"ldo\"])\n",
    "\n",
    "example_parmin=pd.DataFrame(data=np.array([[example_gamma,example_lambda,dustdep,  ventdep, alpha_yr,\n",
    "                  dlambdadz, psi, 1.0]]), index=[0],\n",
    "                  columns=[\"gamma\",\"lambda\",\"sFetop\",\"sFebot\",\"alphabio\",\"dlambdadz\",\"psi\",\"niter\"])\n",
    "\n",
    "example_input=pd.concat((example_timein,example_datain,example_parmin),axis=1)\n",
    "\n",
    "timeseries = pd.DataFrame(\n",
    "                    nutboxmod.model(*[example_input[io].values for io in example_input.columns]),\n",
    "                                     index=[\"t\",\"nso\",\"nna\",\"ndo\",\"fso\",\"fna\",\"fdo\",\"lso\",\"lna\",\"ldo\",\n",
    "                                            \"export1\",\"export2\",\"lim\",\"pstar\"]).transpose()\n",
    "\n",
    "# Make sure initial conditions are included at t~0\n",
    "timeseries[\"t\"][0]=1e-2\n",
    "\n",
    "# Integrated export production\n",
    "timeseries[\"exp\"]=(timeseries[\"export1\"]+timeseries[\"export2\"])*117*86400*365*12*1e-15 # convert molP/s to GtC/yr\n",
    "\n",
    "#Plot the timeseries of...\n",
    "fig3, (f3ax1,f3ax2,f3ax3,f3ax4) = plt.subplots(figsize=(1.5*x_fig, 2.75*y_fig),ncols=1,nrows=4)\n",
    "fig3.patch.set_facecolor('None')\n",
    "mycm = plt.cm.get_cmap(cm.cm.haline)\n",
    "\n",
    "# ...Macronutrient\n",
    "## SO\n",
    "f3ax1.plot(np.log10(timeseries[\"t\"]),timeseries[\"nso\"]*R_np,\n",
    "           color=mycm(240),linewidth=5,label=\"\\\"Southern Ocean\\\"\")\n",
    "# AO\n",
    "f3ax1.plot(np.log10(timeseries[\"t\"]),timeseries[\"nna\"]*R_np,\n",
    "           color=mycm(128),linewidth=5,label=\"\\\"Atlantic Ocean\\\"\")\n",
    "# DO\n",
    "f3ax1.plot(np.log10(timeseries[\"t\"]),timeseries[\"ndo\"]*R_np,\n",
    "           color=mycm(10),linewidth=5,label=\"Deep Ocean\")\n",
    "f3ax1.legend(frameon=False,fontsize=14)\n",
    "f3ax1.set_ylim(top=np.ceil(np.max(f3ax1.get_ylim())/10)*10)\n",
    "f3ax1.set_xlim(left=-3)\n",
    "f3ax1.xaxis.set_major_formatter(FormatStrFormatter('%.1f'))\n",
    "f3ax1.set_ylabel(\"Macronutrient\\nConcentration\\n[mmol m$^{-3}$]\",fontsize=14)\n",
    "f3ax1.text(-5,np.max(f3ax1.get_ylim()),'(a)',fontsize=16)\n",
    "\n",
    "# ...Ligands\n",
    "# SO\n",
    "f3ax2.plot(np.log10(timeseries[\"t\"]),timeseries[\"lso\"],color=mycm(240),linewidth=5,label=\"\\\"Southern Ocean\\\"\")\n",
    "# AO \n",
    "f3ax2.plot(np.log10(timeseries[\"t\"]),timeseries[\"lna\"],color=mycm(128),linewidth=5,label=\"\\\"Atlantic Ocean\\\"\")\n",
    "# DO\n",
    "f3ax2.plot(np.log10(timeseries[\"t\"]),timeseries[\"ldo\"],color=mycm(10),linewidth=5,label=\"Deep Ocean\")\n",
    "f3ax2.legend(frameon=False,fontsize=14)\n",
    "#f3ax2.set_ylim(top=3)\n",
    "f3ax2.set_xlim(left=-3)\n",
    "f3ax2.xaxis.set_major_formatter(FormatStrFormatter('%.1f'))\n",
    "f3ax2.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))\n",
    "f3ax2.set_ylabel(\"Ligand\\nConcentration\\n[$\\mu$mol m$^{-3}$]\",fontsize=14)\n",
    "#f3ax2.set_xlabel(\"Model time [log$_{10}$(yrs)]\",fontsize=14)\n",
    "f3ax2.text(-5,np.max(f3ax2.get_ylim()),'(b)',fontsize=16)\n",
    "\n",
    "# ...Iron\n",
    "# SO\n",
    "f3ax3.plot(np.log10(timeseries[\"t\"]),timeseries[\"fso\"],color=mycm(240),linewidth=5,label=\"\\\"Southern Ocean\\\"\")\n",
    "# AO\n",
    "f3ax3.plot(np.log10(timeseries[\"t\"]),timeseries[\"fna\"],color=mycm(128),linewidth=5,label=\"\\\"Atlantic Ocean\\\"\")\n",
    "# DO\n",
    "f3ax3.plot(np.log10(timeseries[\"t\"]),timeseries[\"fdo\"],color=mycm(10),linewidth=5,label=\"Deep Ocean\")\n",
    "f3ax3.legend(frameon=False,fontsize=14)\n",
    "f3ax3.set_ylim(top=np.ceil(np.max(f3ax3.get_ylim())))\n",
    "f3ax3.set_xlim(left=-3)\n",
    "f3ax3.xaxis.set_major_formatter(FormatStrFormatter('%.1f'))\n",
    "f3ax3.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))\n",
    "f3ax3.set_ylabel(\"Iron\\nConcentration\\n[$\\mu$mol m$^{-3}$]\",fontsize=14)\n",
    "f3ax3.text(-5,np.max(f3ax3.get_ylim()),'(c)',fontsize=16)\n",
    "\n",
    "# ...export\n",
    "f3ax4.plot(np.log10(timeseries[\"t\"]),timeseries[\"exp\"],color='firebrick',linewidth=5,label=\"\\\"Southern\\\"+\\\"Atlantic\\\"\")\n",
    "#f3ax4.legend(frameon=False,fontsize=14)\n",
    "f3ax4.set_ylim(top=np.ceil(np.max(f3ax4.get_ylim())))\n",
    "f3ax4.set_xlim(left=-3)\n",
    "f3ax4.xaxis.set_major_formatter(FormatStrFormatter('%.1f'))\n",
    "f3ax4.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))\n",
    "f3ax4.set_ylabel(\"Total Export\\nProduction\\n[GtC yr$^{-1}$]\",fontsize=14)\n",
    "f3ax4.set_xlabel(\"Model time [log$_{10}$(yrs)]\",fontsize=14)\n",
    "f3ax4.text(-5,np.max(f3ax4.get_ylim()),'(d)',fontsize=16)\n",
    "\n",
    "plt.show()     \n",
    "fig3.savefig('illustration_of_feedback.'+figfmt,format=figfmt,facecolor=fig3.get_facecolor(), edgecolor='none',bbox_inches='tight')\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initially, the model is iron limited globally with elevated macronutrients in both \"Atlantic Ocean\" and \"Southern Ocean\" surface boxes (a). Relatively high iron delivery to the \"Atlantic Ocean\" box leads to an initial rise in productivity (d) and depletion of surface macronutrients. This drives ligand production (b), allowing accumulation of a standing stock of deep ocean iron (c). In the following centuries, macronutrients stay depleted with elevated productivity, and ligand levels converge towards steady state due to transport and loss processes. \n",
    "\n",
    "In contrast, lower iron input to the \"Southern Ocean\" box cannot support rapid macronutrient drawdown. On longer timescales, as ligand levels increase throughout the ocean, upwelled chelated iron drives a gradual incomplete reduction of surface macronutrients. \n",
    "\n",
    "Steady state is reached after 1000 years. The \"Southern Ocean\" box is iron-limited (c) with incomplete macronutrient use (a) fueled by iron supply from the deep ocean, while the \"Atlantic Ocean\" box is macronutrient limited, with sufficient iron to fully consume macronutrients primarily delivered by the overturning circulation.\n",
    "\n",
    "An emergent positive feedback promotes global-scale iron and macronutrient co-limitation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. BOXMODEL ENSEMBLE: Run 10,000 experiments covering a wide range of ligand-iron-microbe feedback values. \n",
    "**(Run new simulations and save the output to a file, or load previous runs from those files.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Assign gamma and lambda parameters systematically to cover parameter space. Use log spacing to get good data coverage at the really small values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#gamma_fe is in phosphate units, not carbon units...divide by RCP=106 to get values referenced in paper\n",
    "# lt_rate is in years, then converted to seconds\n",
    "grid_lt_rate,grid_gamma=np.meshgrid(np.geomspace(0.1,1000,nincs),np.geomspace(1e-5,10,nincs))\n",
    "gamma_fe = grid_gamma.flatten()\n",
    "lt_rate  = grid_lt_rate.flatten()*3.0e7\n",
    "\n",
    "gamma_over_lambda=(gamma_fe[:niters]/R_cp)/(1/lt_rate[:niters]) # Lambda needs to be 1/s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Assign random initial values of macronutrients, iron, and ligands**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Phosphate has to be conserved (convert to mol/m3 before volume integrating)\n",
    "pinv=nm.sum(np.array((2.1, 2.1, 2.1)) * conv * 1.0e-6 * vol)\n",
    "\n",
    "# Each row of boxfrac sums to 1 (also generate random value for control run with fixed, uniform ligand concentration)\n",
    "boxfrac=np.random.dirichlet(np.ones(3),size=(niters))\n",
    "\n",
    "# Each row of parray now contains randomly assigned fractions of the same total phosphate pool (umol/kg)\n",
    "parray=(boxfrac*pinv)/(vol*conv*1e-6)\n",
    "\n",
    "# Remainder of initial conditions randomly allocated as nmol/kg (also generate random value for control run)\n",
    "farray=np.random.randint(0,100,size=(niters,3)).astype(np.double)\n",
    "larray=np.random.randint(0,100,size=(niters,3)).astype(np.double)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Define global input/output arrays**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a consolidated Pandas dataframe with the input values\n",
    "pd_datain=pd.DataFrame(data=np.concatenate((\n",
    "                    parray*1.0e-6,farray*1.0e-9,larray*1.0e-9),axis=1),\n",
    "                    columns=[\"nso\",\"nna\",\"ndo\",\"fso\",\"fna\",\"fdo\",\"lso\",\"lna\",\"ldo\"])\n",
    "pd_datain.name=\"input_conc\"\n",
    "pd_parmin=pd.DataFrame(data=np.concatenate((gamma_fe[:,np.newaxis],lt_rate[:,np.newaxis],\n",
    "                    np.tile(dustdep,(niters,1)),np.tile(ventdep,(niters,1)),np.tile(alpha_yr,(niters,1)),\n",
    "                    np.tile(dlambdadz,(niters,1)),np.tile(psi,(niters,1)),np.arange(0,niters)[:,np.newaxis]),axis=1),\n",
    "                    columns=[\"gamma\",\"lambda\",\"sFetop\",\"sFebot\",\"alphabio\",\"dlambdadz\",\"psi\",\"niter\"])\n",
    "pd_parmin.name=\"input_parameters\"\n",
    "pd_aux   =pd.DataFrame(data=np.concatenate((gamma_over_lambda[:,np.newaxis],\n",
    "                    np.tile(area[0],(niters,1)),np.tile(area[1],(niters,1)),np.tile(area[2],(niters,1)),\n",
    "                    np.tile(ninit,(niters,1))),axis=1),\n",
    "                    columns=[\"gamma_over_lambda\",\"areaso\",\"areana\",\"areado\",\"ninit\"])\n",
    "pd_aux.name=\"auxilery_data\"\n",
    "pd_input=pd.concat((pd_datain,pd_aux.areaso*0,pd_aux.areana*0,pd_aux.areado*0,pd_aux.ninit*0),axis=1)\n",
    "pd_input.rename(columns={\"areaso\":\"export1\",\"areana\":\"export2\",\"areado\":\"lim\",\"ninit\":\"pstar\"},inplace=True)\n",
    "pd_input.name=\"input_dataset\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Run the model or read in previously saved model output**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running new model ensemble...in parallel\n",
      "INFO: Pandarallel will run on 8 workers.\n",
      "INFO: Pandarallel will use standard multiprocessing data transfer (pipe) to transfer data between the main process and workers.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "22ccb920e9214def98570fdfe80825b3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=1250), Label(value='0 / 1250'))), …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41695.741669893265 seconds\n"
     ]
    }
   ],
   "source": [
    "if RUNMODEL: # Only run the model if true.\n",
    "    ## Write input values to a file for future reference (not the control)\n",
    "    pd.concat((pd_input.drop({\"export1\",\"export2\",\"lim\",\"pstar\"},axis=1),\n",
    "               pd_parmin[\"gamma\"],\n",
    "               pd_parmin[\"lambda\"]**-1,\n",
    "               pd_aux[\"gamma_over_lambda\"]),axis=1) \\\n",
    "              .rename(columns={\"lambda\":\"1/lambda\"})\\\n",
    "              .to_csv(finname, index = False, header=True)\n",
    "    \n",
    "    # The initial conditions are also written out at the start of the model output   \n",
    "#    print(\"Running new model ensemble...this may take some time, O(2days) for 10,000 simulations\")\n",
    "    print(\"Running new model ensemble...in parallel\")\n",
    "    import time\n",
    "    start = time.time()\n",
    "    if PARALLEL:\n",
    "        # Run in parallel using pkg/pandarallel's \"parallel_apply\"\n",
    "        import multiprocessing\n",
    "        ensemble_output=utils.run_boxmodel(pd_input,pd_parmin,pd_aux,pd_refs,\n",
    "                                                    workers=multiprocessing.cpu_count(),\n",
    "                                                    Rcp=R_cp,Rnp=R_np)\n",
    "    else:\n",
    "        # OR in series using pandas' \"apply\" to run through rows of a data frame of inputs \n",
    "        ensemble_output=utils.run_boxmodel(pd_input,pd_parmin,pd_aux,pd_refs,\n",
    "                                                    workers=1,\n",
    "                                                    Rcp=R_cp,Rnp=R_np)\n",
    "    end = time.time()\n",
    "    print(\"{0} seconds\".format((end - start)))\n",
    "\n",
    "    ## Save the output as another csv file\n",
    "    ensemble_output[[\"nso\",\"nna\",\"ndo\",\"fso\",\"fna\",\"fdo\",\"lso\",\"lna\",\"ldo\",\"ncost\",\"fcost\",\"lcost\",\"lim\",\"export1\",\"export2\",\"pstar\"]] \\\n",
    "              .rename(columns={\"lim\":\"nlimit\"})\\\n",
    "              .to_csv(foutname, index = False, header=True)\n",
    "else:\n",
    "# Load the results from file\n",
    "    try:\n",
    "        print(\"Loading previous model ensemble from CSV files \"+finname+\" and \"+foutname+\"...\")\n",
    "        ensemble_input = pd.read_csv(finname,skiprows=0,header=0, \n",
    "                           names=[\"n1\",\"n2\",\"n3\",\"f1\",\"f2\",\"f3\",\"l1\",\"l2\",\"l3\",\"gamma\",\"inv_lambda\",\"gamma_over_lambda\"])\n",
    "        \n",
    "        ensemble_output = pd.read_csv(foutname,skiprows=0,header=0, \n",
    "                           names=[\"nso\",\"nna\",\"ndo\",\"fso\",\"fna\",\"fdo\",\"lso\",\"lna\",\"ldo\",\"ncost\",\"fcost\",\"lcost\",\"nlimit\",\"export1\",\"export2\",\"pstar\"])\n",
    "        \n",
    "        #ensemble_aux = pd.DataFrame(data=np.concatenate((gamma_over_lambda[:,np.newaxis],\n",
    "        #            np.tile(area[0],(niters,1)),np.tile(area[1],(niters,1)),np.tile(area[2],(niters,1)),\n",
    "        #            np.tile(ninit,(niters,1))),axis=1),\n",
    "        #            columns=[\"gamma_over_lambda\",\"areaso\",\"areana\",\"areado\",\"ninit\"])\n",
    "        ensemble_aux = pd.concat((ensemble_input.gamma_over_lambda,pd_aux.areaso,pd_aux.areana,pd_aux.areado,pd_aux.ninit),axis=1)\n",
    "        ensemble_aux.name=\"auxilery_data\"\n",
    "        \n",
    "        ensemble_output = utils.calc_boxmodel_vars(ensemble_output,ensemble_aux,pd_refs,Rcp=R_cp)\n",
    "        ensemble_output = pd.concat((ensemble_output,ensemble_input.gamma,\n",
    "                                     1/ensemble_input.inv_lambda,\n",
    "                                     ensemble_input.gamma_over_lambda),axis=1).rename(columns={\"inv_lambda\":\"lambda\"})\n",
    "    except FileNotFoundError:\n",
    "        try:\n",
    "            # This is the real legacy method, with seperate output files for each run\n",
    "            ensemble_input = pd.read_csv(finname,skiprows=0,header=0, \n",
    "                           names=[\"n1\",\"n2\",\"n3\",\"f1\",\"f2\",\"f3\",\"l1\",\"l2\",\"l3\",\"gamma\",\"inv_lambda\",\"gamma_over_lambda\"])\n",
    "            \n",
    "            print(\"Loading previous model ensemble from model files...\")\n",
    "            \n",
    "            # Split pandas DF into seperate columns\n",
    "            for ivar in np.arange(len(data_inp.columns)):\n",
    "                locals()[ensemble_input.columns[ivar]]=ensemble_input[ensemble_input.columns[ivar]].to_numpy(copy=True)[:,np.newaxis]\n",
    "            \n",
    "            fname='ironmodel'\n",
    "            utils.read_boxmodel_iter(ninit,niters,nref,nstd,fref,fstd,lref,lstd,area,fprefix=fname,Rnp=R_np)\n",
    "            \n",
    "            \n",
    "        except FileNotFoundError:\n",
    "            print('Could not find CSV or DAT model output! Confirm filenames or set RUNMODEL to \"True\"')\n",
    "            raise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. ANALYSIS: Calculate observational range, model-data comparison score, bulk ligand residence time, and uniform ligand benchmark value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run a control experiment with fixed 1nm ligand concentration\n",
    "cntrl_nt=1e4\n",
    "cntrl_dt=10\n",
    "\n",
    "pcntrl=(np.random.dirichlet(np.ones(3),size=(1))*pinv)/(vol*conv*1e-6)\n",
    "fcntrl=np.random.randint(0,100,size=(1,3)).astype(np.double)\n",
    "lcntrl=np.tile(fixedligconc,(1,3))\n",
    "\n",
    "# Gamma and lambda are zero because ligand concentration is uniform and constant\n",
    "cntrl_gamma  = 0.0\n",
    "cntrl_lambda = 0.0\n",
    "\n",
    "cntrl_timein=pd.DataFrame(data=np.concatenate(\n",
    "                    (np.array((cntrl_nt,)),np.array((cntrl_dt,))),axis=0)[np.newaxis,:],\n",
    "                    columns=[\"nyrs\",\"tout\"])\n",
    "\n",
    "cntrl_datain=pd.DataFrame(data=np.concatenate((\n",
    "                    pcntrl*1.0e-6,fcntrl*1.0e-9,lcntrl*1.0e-9),axis=1),\n",
    "                    columns=[\"n1\",\"n2\",\"n3\",\"f1\",\"f2\",\"f3\",\"l1\",\"l2\",\"l3\"])\n",
    "\n",
    "cntrl_parmin=pd.DataFrame(data=np.array([[cntrl_gamma,cntrl_lambda,dustdep,  ventdep, alpha_yr,\n",
    "                  dlambdadz, psi, 1.0]]), index=[0],\n",
    "                  columns=[\"gamma\",\"lambda\",\"sFetop\",\"sFebot\",\"alphabio\",\"dlambdadz\",\"psi\",\"niter\"])\n",
    "\n",
    "cntrl_input=pd.concat((cntrl_timein,cntrl_datain,cntrl_parmin),axis=1)\n",
    "\n",
    "cntrl_output = pd.DataFrame(\n",
    "                    nutboxmod.model(*[cntrl_input[io].values for io in cntrl_input.columns]),\n",
    "                    index=[\"t\",\"nso\",\"nna\",\"ndo\",\"fso\",\"fna\",\"fdo\",\"lso\",\"lna\",\"ldo\",\n",
    "                            \"export1\",\"export2\",\"lim\",\"pstar\"]).transpose() \\\n",
    "                    .iloc[:np.int((cntrl_nt/cntrl_dt)+1)] # only return the filled part of the array\n",
    "\n",
    "cntrl_output['ncost']=utils.calc_cost(((cntrl_output[[\"nso\",\"nna\",\"ndo\"]]*R_np).T.values),\n",
    "                           (pd_refs.filter(regex='^n[a-zA-Z]{2}ref').values),\n",
    "                           (pd_refs.filter(regex='^n[a-zA-Z]{2}std').values))\n",
    "cntrl_output['fcost']=utils.calc_cost((cntrl_output[[\"fso\",\"fna\",\"fdo\"]].T.values),\n",
    "                           (pd_refs.filter(regex='^f[a-zA-Z]{2}ref').values),\n",
    "                           (pd_refs.filter(regex='^f[a-zA-Z]{2}std').values))\n",
    "cntrl_output['lcost']=utils.calc_cost((cntrl_output[[\"lso\",\"lna\",\"ldo\"]].T.values),\n",
    "                           (pd_refs.filter(regex='^l[a-zA-Z]{2}ref').values),\n",
    "                           (pd_refs.filter(regex='^l[a-zA-Z]{2}std').values))\n",
    "\n",
    "# Model-data comparison score\n",
    "# number of obs used in objective function - 3 boxes and 3 variables (weighted equally)\n",
    "nobs=pd_refs.filter(regex='^[a-zA-Z]{3}ref').count().count()\n",
    "cntrl_output['jovern']  = np.exp(-1*(cntrl_output['ncost']+cntrl_output['fcost']+cntrl_output['lcost'])/nobs) \n",
    "# Seperate contributions (multiply to recover full score)\n",
    "cntrl_output['jovernn'] = np.exp(-1*(cntrl_output['ncost'])/nobs) \n",
    "cntrl_output['jovernf'] = np.exp(-1*(cntrl_output['fcost'])/nobs) \n",
    "cntrl_output['jovernl'] = np.exp(-1*(cntrl_output['lcost'])/nobs) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the data-based constraint of gamma/lambda\n",
    "data_goverl,range_goverl=utils.calc_gamma_over_lambda_range()\n",
    "\n",
    "# What is an estimate of ligand residence time?\n",
    "# Order of magnitude for gamma is 1e-5 to 1e-4 (Voelker and Tagliabue, 2015)\n",
    "ltrest=np.sort(np.array((\n",
    "        np.min((np.tile(10**data_goverl,(2,1))/np.tile(np.array((1e-5,1e-4))[:,np.newaxis],(1,2)))),\n",
    "        np.max((np.tile(10**data_goverl,(2,1))/np.tile(np.array((1e-5,1e-4))[:,np.newaxis],(1,2))))\n",
    "        ))/(86400*365))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reduce the dimensionality of the model output by averaging in gamma_over_lambda space (i.e. according to equal/similar values for gamma/lambda)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "ensemble_output[\"gol_bins\"]=pd.cut(ensemble_output[\"gamma_over_lambda\"],\n",
    "                                 np.geomspace(0.1,1e10,np.min((2*nincs,256))))\n",
    "ensemble_output[\"gaovla_average\"]=[(a.left + a.right)/2 for a in ensemble_output[\"gol_bins\"]]\n",
    "\n",
    "# Calculate median and median absolute deviation (simliar results from mean and standard deviation)\n",
    "df_average=ensemble_output.groupby([\"gol_bins\"],sort=False).median()\n",
    "df_spread =ensemble_output.groupby([\"gol_bins\"],sort=False).agg(utils.mad) # Pandas is MEAN abs dev, not MEDIAN!\n",
    "\n",
    "\n",
    "# Region of gamma/lambda where macro- and micro-nutrients co-limit production in different spatial regions\n",
    "#colimits=np.array((\\\n",
    "#       np.min(np.log10(gaovla_average[np.log10(nso_average)==np.nanmin(np.log10(nso_average))])),\n",
    "#       np.max(np.log10(gaovla_average[exp_average<=2.0]))\n",
    "#       ))\n",
    "# Look for the limits where export production reaches high (near-maximum) levels, ie lifted global iron limitation, \n",
    "# but before global surface nutrients become completely consumed, ie becoming globally iron replete\n",
    "colimits=np.array((np.log10((\\\n",
    "       df_average['gaovla_average'].where(np.isclose(df_average['exportt'], df_average['exportt'].max(), rtol=0.1)).min(),\n",
    "       df_average[\"gaovla_average\"].where(df_average['nso'].round(8)==df_average['nso'].round(8).min()).min()\n",
    "       ))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. PLOTS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 4: 10,000 model simulations nutrient and export fluxes**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1280x320 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid_gaovla=   np.reshape(ensemble_output[\"gamma_over_lambda\"].values,np.shape(grid_gamma))\n",
    "# Have to tinker with these values because they require unit conversions\n",
    "grid_gam   =   np.reshape(ensemble_output[\"gamma\"].values/R_cp,np.shape(grid_gamma))\n",
    "grid_lt    = 1/(np.reshape(ensemble_output[\"lambda\"].values,np.shape(grid_gamma)))\n",
    "\n",
    "fig4, (f4ax1,f4ax2,f4ax3) = plt.subplots(figsize=(4*x_fig, y_fig),ncols=3,gridspec_kw={'width_ratios': [1.4, 1.2, 1.2]})\n",
    "fig4.patch.set_facecolor('None')\n",
    "mycm = plt.cm.get_cmap(cm.cm.haline)\n",
    "\n",
    "f4ax1c1=f4ax1.contourf(np.log10(grid_gam),np.log10(grid_lt),np.log10(grid_gaovla),np.arange(0,11,1),cmap=mycm,vmin=0,vmax=10,extend='both')\n",
    "# This is the fix for the white lines between contour levels\n",
    "for a in f4ax1.collections:\n",
    "    a.set_edgecolor(\"face\")\n",
    "\n",
    "f4cbar1=fig4.colorbar(f4ax1c1,ax=f4ax1,ticks=np.arange(0,12.0,2.0))\n",
    "f4cbar1.solids.set_edgecolor(\"face\")\n",
    "f4cbar1.set_label('log$_{10}(\\gamma/\\lambda)$ [s]',fontsize=14)\n",
    "f4ax1.set_ylabel('log$_{10}(\\lambda)$ [s$^{-1}$]',fontsize=14)\n",
    "f4ax1.set_xlabel('log$_{10}(\\gamma)$ [mol mol$^{-1}$]',fontsize=14)\n",
    "f4ax1.set_xlim(left=-7,right=-1)\n",
    "f4ax1.xaxis.set_ticks(np.arange(-7,0,1))\n",
    "f4ax1.set_ylim(bottom=-10.5,top=-6.5)\n",
    "f4ax1.yaxis.set_ticks(np.arange(-10.5,-6,0.5))\n",
    "f4ax1.xaxis.set_major_formatter(FormatStrFormatter('%.1f'))\n",
    "f4ax1.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))\n",
    "f4ax1.text(-8.5,-5.75,'(a)',fontsize=16)\n",
    "\n",
    "# Macronutrients\n",
    "f4ax2.plot(np.log10(df_average[\"gaovla_average\"].values),np.log10(df_average[\"nsurf\"]-df_spread[\"nsurf\"]),color=mycm(10))\n",
    "f4ax2.plot(np.log10(df_average[\"gaovla_average\"].values),np.log10(df_average[\"nsurf\"]+df_spread[\"nsurf\"]),color=mycm(10))\n",
    "f4ax2.fill_between(np.log10(df_average[\"gaovla_average\"].values),np.log10(df_average[\"nsurf\"]-df_spread[\"nsurf\"]),np.log10(df_average[\"nsurf\"]+df_spread[\"nsurf\"]),color=mycm(50))\n",
    "\n",
    "f4ax2.set_xlabel('log$_{10}(\\gamma/\\lambda)$ [s]',fontsize=14)\n",
    "f4ax2.set_xlim(left=-4,right=10)\n",
    "f4ax2.xaxis.set_ticks(np.arange(-4,12,2))\n",
    "f4ax2.xaxis.set_major_formatter(FormatStrFormatter('%.1f'))\n",
    "f4ax2.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))\n",
    "f4ax2.set_ylim(bottom=-2,top=2)\n",
    "\n",
    "# Change plot appearence\n",
    "if R_np==16:\n",
    "    f4ax2.set_ylabel('Surface-average Nitrate\\nConcentration [log$_{10}$(mmol m$^{-3}$)]',fontsize=14)\n",
    "    f4ax2.text(np.min(np.log10(df_average[\"gaovla_average\"])),np.max(f4ax2.get_ylim())-0.25,'Nitrate',color=mycm(10),fontsize=14)\n",
    "else:\n",
    "    f4ax2.set_ylabel('Surface-average Phosphate\\nConcentration [log$_{10}$(mmol m$^{-3}$)]',fontsize=14)\n",
    "    f4ax2.text(np.log10(df_average[\"gaovla_average\"]),np.max(f4ax2.get_ylim())-0.3,'Phosphate',color=mycm(10),fontsize=14)\n",
    "\n",
    "\n",
    "# Second axes for Macronutrients that shares the same x-axis\n",
    "f4ax2a = f4ax2.twinx()  \n",
    "# Ligands\n",
    "f4ax2a.plot(np.log10(df_average[\"gaovla_average\"]),np.log10(df_average[\"lsurf\"]-df_spread[\"lsurf\"]),color=mycm(128))\n",
    "f4ax2a.plot(np.log10(df_average[\"gaovla_average\"]),np.log10(df_average[\"lsurf\"]+df_spread[\"lsurf\"]),color=mycm(128))\n",
    "f4ax2a.fill_between(np.log10(df_average[\"gaovla_average\"]),np.log10(df_average[\"lsurf\"]-df_spread[\"lsurf\"]),np.log10(df_average[\"lsurf\"]+df_spread[\"lsurf\"]),color=mycm(150))\n",
    "# Iron\n",
    "f4ax2a.plot(np.log10(df_average[\"gaovla_average\"]),np.log10(df_average[\"fsurf\"]-df_spread[\"fsurf\"]),'firebrick')\n",
    "f4ax2a.plot(np.log10(df_average[\"gaovla_average\"]),np.log10(df_average[\"fsurf\"]+df_spread[\"fsurf\"]),'firebrick')\n",
    "f4ax2a.fill_between(np.log10(df_average[\"gaovla_average\"]),np.log10(df_average[\"fsurf\"]-df_spread[\"fsurf\"]),np.log10(df_average[\"fsurf\"]+df_spread[\"fsurf\"]),color='salmon')\n",
    "\n",
    "# Change plot appearence\n",
    "f4ax2a.set_ylim(bottom=-6,top=6)\n",
    "f4ax2a.set_ylabel('Surface-average Iron and Ligand\\nConcentration [log$_{10}$($\\mu$mol m$^{-3}$)]',fontsize=14)\n",
    "f4ax2a.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))\n",
    "\n",
    "# Co limitation zone\n",
    "f4ax2a.fill_between((colimits[0],colimits[1]),\n",
    "                  np.min(f4ax2a.get_ylim()), np.max(f4ax2a.get_ylim()),color='#cccccc',zorder=-1)\n",
    "\n",
    "#Change the overlay order\n",
    "f4ax2.set_zorder(f4ax2.get_zorder()+1)\n",
    "f4ax2a.patch.set_visible(True)\n",
    "f4ax2.patch.set_visible(False)\n",
    "\n",
    "# Labels\n",
    "f4ax2a.text(-7,np.max(f4ax2a.get_ylim())+2.0,'(b)',fontsize=16)\n",
    "f4ax2a.text(7,np.max(f4ax2a.get_ylim())-3.5,'Iron',color='firebrick',fontsize=14)\n",
    "f4ax2a.text(6,np.max(f4ax2a.get_ylim())-.75,'Ligand',color=mycm(128),fontsize=14)\n",
    "f4ax2a.text(8,np.max(f4ax2a.get_ylim())+.5,'Iron replete\\n(macronutrient\\nlimited)',horizontalalignment='center',fontsize=14)\n",
    "f4ax2a.text((colimits[1]+colimits[0])/2,np.max(f4ax2a.get_ylim())+0.6,'Co-\\nlimited',rotation=-90,horizontalalignment='center',fontsize=14)\n",
    "f4ax2a.text(-2.1,np.max(f4ax2a.get_ylim())+.5,'Iron limited\\n(macronutrient\\nreplete)',horizontalalignment='center',fontsize=14)\n",
    "\n",
    "# Plot data-based constraint of gamma/lambda\n",
    "f4ax2a.annotate(text='', xy=(np.max(data_goverl),np.min(f4ax2a.get_ylim())), xytext=(np.min(data_goverl),np.min(f4ax2a.get_ylim())), arrowprops=dict(color='black',arrowstyle='<|-|>,head_width=0.5,head_length=0.5', shrinkA=0, shrinkB=0, linewidth=2))\n",
    "\n",
    "# Plot median export values in each basin\n",
    "f4ax3.plot(np.log10(df_average[\"gaovla_average\"]),(df_average[\"export1\"]-df_spread[\"export1\"])*R_cp*86400*365*12*1e-15,color=mycm(10))\n",
    "f4ax3.plot(np.log10(df_average[\"gaovla_average\"]),(df_average[\"export1\"]+df_spread[\"export1\"])*R_cp*86400*365*12*1e-15,color=mycm(10),label=\"Southern\")\n",
    "f4ax3.fill_between(np.log10(df_average[\"gaovla_average\"]),(df_average[\"export1\"]-df_spread[\"export1\"])*R_cp*86400*365*12*1e-15,\n",
    "                                                          (df_average[\"export1\"]+df_spread[\"export1\"])*R_cp*86400*365*12*1e-15,\n",
    "                                                           color=mycm(50))\n",
    "\n",
    "f4ax3.plot(np.log10(df_average[\"gaovla_average\"]),(df_average[\"export2\"]-df_spread[\"export2\"])*R_cp*86400*365*12*1e-15,color=mycm(128))\n",
    "f4ax3.plot(np.log10(df_average[\"gaovla_average\"]),(df_average[\"export2\"]+df_spread[\"export2\"])*R_cp*86400*365*12*1e-15,color=mycm(128),label=\"Northern\")\n",
    "f4ax3.fill_between(np.log10(df_average[\"gaovla_average\"]),(df_average[\"export2\"]-df_spread[\"export2\"])*R_cp*86400*365*12*1e-15,\n",
    "                                                          (df_average[\"export2\"]+df_spread[\"export2\"])*R_cp*86400*365*12*1e-15,\n",
    "                                                           color=mycm(150))\n",
    "\n",
    "f4ax3.plot(np.log10(df_average[\"gaovla_average\"]),df_average[\"exportt\"]-df_spread[\"exportt\"],'firebrick')\n",
    "f4ax3.plot(np.log10(df_average[\"gaovla_average\"]),df_average[\"exportt\"]+df_spread[\"exportt\"],'firebrick',label=\"Total\")\n",
    "f4ax3.fill_between(np.log10(df_average[\"gaovla_average\"]),(df_average[\"exportt\"]-df_spread[\"exportt\"]),\n",
    "                                                          (df_average[\"exportt\"]+df_spread[\"exportt\"]),\n",
    "                                                           color='salmon')\n",
    "\n",
    "# Change plot appearence\n",
    "f4ax3.set_ylabel('Export Production [GtC yr$^{-1}$]',fontsize=14)\n",
    "f4ax3.set_xlabel('log$_{10}$($\\gamma/\\lambda$) [s]',fontsize=14)\n",
    "f4ax3.set_xlim(left=-4,right=10)\n",
    "f4ax3.xaxis.set_ticks(np.arange(-4,12,2))\n",
    "f4ax3.set_ylim(bottom=0.0,top=2.5)\n",
    "f4ax3.xaxis.set_major_formatter(FormatStrFormatter('%.1f'))\n",
    "f4ax3.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))\n",
    "\n",
    "# Plot data-based constraint of gamma/lambda\n",
    "f4ax3.annotate(text='', xy=(np.max(data_goverl),np.min(f4ax3.get_ylim())), xytext=(np.min(data_goverl),np.min(f4ax3.get_ylim())), arrowprops=dict(color='black',arrowstyle='<|-|>,head_width=0.5,head_length=0.5', shrinkA=0, shrinkB=0, linewidth=2))\n",
    "\n",
    "f4ax3.text(-7,np.max(f4ax3.get_ylim())+0.5,'(c)',fontsize=16)\n",
    "f4ax3.text(7.5,2.25,'Total',color='firebrick',horizontalalignment='center',fontsize=14)\n",
    "f4ax3.text(7.5,1.5,'\\\"Southern\\nOcean\\\"',color=mycm(10),horizontalalignment='center',fontsize=14)\n",
    "f4ax3.text(7.5,0.2,'\\\"Atlantic\\nOcean\\\"',color=mycm(128),horizontalalignment='center',fontsize=14)\n",
    "f4ax3.text(8,1.04*np.max(f4ax3.get_ylim()),'Iron replete\\n(macronutrient\\nlimited)',horizontalalignment='center',fontsize=14)\n",
    "f4ax3.text((colimits[1]+colimits[0])/2,1.05*np.max(f4ax3.get_ylim()),'Co-\\nlimited',rotation=-90,horizontalalignment='center',fontsize=14)\n",
    "f4ax3.text(-2,1.04*np.max(f4ax3.get_ylim()),'Iron limited\\n(macronutrient\\nreplete)',horizontalalignment='center',fontsize=14)\n",
    "\n",
    "# Co limitation zone\n",
    "f4ax3.fill_between((colimits[0],colimits[1]),\n",
    "                  np.min(f4ax3.get_ylim()), np.max(f4ax3.get_ylim()),color='#cccccc',zorder=-1)\n",
    "\n",
    "# Can adjust the subplot size\n",
    "plt.subplots_adjust(wspace=0.5)\n",
    "\n",
    "# Manuallly move fig (b) left a bit\n",
    "pos = f4ax2.get_position().bounds\n",
    "tmp=pos[0]-0.075*pos[2]\n",
    "f4ax2.set_position([tmp,pos[1],pos[2],pos[3]])\n",
    "\n",
    "plt.show()     \n",
    "fig4.savefig('ensemble_nutrients_export_plot.'+figfmt,format=figfmt,facecolor=fig4.get_facecolor(), edgecolor='none',bbox_inches='tight')\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is notable that the concentrations and rates of microbial production converge to a tight curve as a function of gamma/lambda while representing numerous combinations of individual gamma and lambda values, as well as a range of random arbitrary initial conditions. In other words, the outcomes are robust and predictable for any given gamma/lambda, and independent of initial conditions: the feedback restores the degree of biological limitation between macronutrients and iron for the specific value of gamma/lambda.\n",
    "\n",
    "Guided by these data, the experiments can be partitioned into three regimes: \n",
    "(i) iron-replete (macronutrient limited) simulations, (ii) iron-limited (macronutrient replete) simulations, and (iii) iron and macronutrient co-limited simulations (region shaded grey)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 5: 10,000 model simulations model-data comparison**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1280x320 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "jgrid =np.reshape(ensemble_output[\"jovern\"].values,np.shape(grid_gamma))\n",
    "pgrid =np.reshape(ensemble_output[\"pstar\"] .values,np.shape(grid_gamma))\n",
    "\n",
    "#fig5, (f5ax1,f5ax2) = plt.subplots(figsize=(2.5*x_fig, y_fig),ncols=2,gridspec_kw={'width_ratios': [1.4,1.2]})\n",
    "fig5, (f5ax1,f5ax2,f5ax3) = plt.subplots(figsize=(4*x_fig, y_fig),ncols=3,gridspec_kw={'width_ratios': [1.4, 1.2, 1.2]})\n",
    "\n",
    "fig5.patch.set_facecolor('None')\n",
    "\n",
    "f5ax1c1=f5ax1.contourf(np.log10(grid_gam),np.log10(grid_lt),jgrid,np.arange(0,0.85,0.05),cmap=mycm,vmin=0,vmax=0.8,extend='max')\n",
    "for a in f5ax1.collections:\n",
    "    a.set_edgecolor(\"face\")\n",
    "f5cbar1=fig5.colorbar(f5ax1c1,ax=f5ax1,ticks=np.arange(0,0.9,0.1))\n",
    "f5cbar1.solids.set_edgecolor(\"face\")\n",
    "f5cbar1.set_label('Model-data comparison score (S)',fontsize=14)\n",
    "\n",
    "# Contour macronutrient use efficiency\n",
    "f5ax1c2=f5ax1.contour(np.log10(grid_gam),np.log10(grid_lt),pgrid,levels=(0.2,0.4,0.9),colors='gray')\n",
    "f5ax1.clabel(f5ax1c2,(0.2,0.4,0.9), inline=True, fmt='%.1f', fontsize=12,manual=[(-5.5, -8.25), (-4.75,-8.5), (-4,-8.5)],colors='gray')\n",
    "\n",
    "f5ax1.set_ylabel('log$_{10}(\\lambda)$ [s$^{-1}$]',fontsize=14)\n",
    "f5ax1.set_xlabel('log$_{10}(\\gamma)$ [mol mol$^{-1}$]',fontsize=14)\n",
    "f5ax1.set_xlim(left=-7,right=-1)\n",
    "f5ax1.xaxis.set_ticks(np.arange(-7,0,1))\n",
    "f5ax1.set_ylim(bottom=-10.5,top=-6.5)\n",
    "f5ax1.yaxis.set_ticks(np.arange(-10.5,-6,0.5))\n",
    "f5ax1.xaxis.set_major_formatter(FormatStrFormatter('%.1f'))\n",
    "f5ax1.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))\n",
    "f5ax1.text(-8.5,-5.75,'(a)',fontsize=16)\n",
    "\n",
    "# Plot vertical lines for lambda/gamma value that minimizes the cost function\n",
    "f5ax2.axvline(np.log10(df_average[\"gaovla_average\"].where(df_average[\"jovern\"]==df_average[\"jovern\"].max()).max()),linestyle='--',color='black')\n",
    "\n",
    "# Plot Objective function values\n",
    "f5ax2.plot(np.log10(df_average[\"gaovla_average\"]),df_average[\"jovern\"]-df_spread[\"jovern\"],color=mycm(10))\n",
    "f5ax2.plot(np.log10(df_average[\"gaovla_average\"]),df_average[\"jovern\"]+df_spread[\"jovern\"],color=mycm(10))\n",
    "f5ax2.fill_between(np.log10(df_average[\"gaovla_average\"]), df_average[\"jovern\"]-df_spread[\"jovern\"],df_average[\"jovern\"]+df_spread[\"jovern\"],color=mycm(50))\n",
    "     \n",
    "# Change plot appearence\n",
    "f5ax2.set_ylabel('Model-data comparison score (S)',fontsize=14)\n",
    "f5ax2.set_xlabel('log$_{10}$($\\gamma/\\lambda$) [s]',fontsize=14)\n",
    "f5ax2.set_xlim(left=-4,right=10)\n",
    "f5ax2.xaxis.set_ticks(np.arange(-4,12,2))\n",
    "f5ax2.set_ylim(bottom=-0.05,top=1.05)\n",
    "f5ax2.xaxis.set_major_formatter(FormatStrFormatter('%.1f'))\n",
    "\n",
    "# Plot horizontal line where cost function uses the value for 1nm fixed ligand concentration\n",
    "f5ax2.axhline(cntrl_output['jovern'].tail(1).values,linestyle='--',color=mycm(10))\n",
    "\n",
    "# Plot data-based constraint of gamma/lambda\n",
    "f5ax2.annotate(text='', xy=(np.max(data_goverl),np.min(f5ax2.get_ylim())), xytext=(np.min(data_goverl),np.min(f5ax2.get_ylim())), arrowprops=dict(color='black',arrowstyle='<|-|>,head_width=0.5,head_length=0.5', shrinkA=0, shrinkB=0, linewidth=2))\n",
    "\n",
    "f5ax2.text(-7,1.25,'(b)',fontsize=16)\n",
    "f5ax2.text(8,1.09,'Iron replete\\n(macronutrient\\nlimited)',horizontalalignment='center',fontsize=14)\n",
    "f5ax2.text((colimits[1]+colimits[0])/2,1.09,'Co-\\nlimited',rotation=-90,horizontalalignment='center',fontsize=14)\n",
    "f5ax2.text(-2,1.09,'Iron limited\\n(macronutrient\\nreplete)',horizontalalignment='center',fontsize=14)\n",
    "\n",
    "# Co limitation zone\n",
    "f5ax2.fill_between((colimits[0],colimits[1]),\n",
    "                  np.min(f5ax2.get_ylim()), np.max(f5ax2.get_ylim()),color='#cccccc',zorder=-1)\n",
    "\n",
    "# Plot vertical lines for lambda/gamma value that minimizes the cost function\n",
    "f5ax3.axvline(np.log10(df_average[\"gaovla_average\"].where(df_average[\"jovern\"]==df_average[\"jovern\"].max()).max()),linestyle='--',color='black')\n",
    "f5ax3.plot(np.log10(df_average[\"gaovla_average\"]),(df_average[\"pstar\"]-df_spread[\"pstar\"]),color='firebrick',linestyle='-.')\n",
    "f5ax3.plot(np.log10(df_average[\"gaovla_average\"]),(df_average[\"pstar\"]+df_spread[\"pstar\"]),color='firebrick',linestyle='-.')\n",
    "f5ax3.fill_between(np.log10(df_average[\"gaovla_average\"]),(df_average[\"pstar\"]-df_spread[\"pstar\"]),(df_average[\"pstar\"]+df_spread[\"pstar\"]),color='salmon')\n",
    "\n",
    "# Change plot appearence\n",
    "f5ax3.set_ylabel('Nutrient usage efficiency [fraction]',fontsize=14)  \n",
    "f5ax3.set_xlabel('log$_{10}$($\\gamma/\\lambda$) [s]',fontsize=14)\n",
    "f5ax3.set_xlim(left=-4,right=10)\n",
    "f5ax3.xaxis.set_ticks(np.arange(-4,12,2))\n",
    "f5ax3.set_ylim(bottom=-0.05,top=1.05)\n",
    "f5ax3.xaxis.set_major_formatter(FormatStrFormatter('%.1f'))\n",
    "\n",
    "# Optimum model macronutrient usage efficiency\n",
    "#f5ax3.annotate(text='', xy=(np.max(f5ax3.get_xlim()),pstar_average[np.nanargmax(jovern_average)],), xytext=(np.log10(gaovla_average[np.nanargmax(jovern_average)]),pstar_average[np.nanargmax(jovern_average)]), arrowprops=dict(color='firebrick',linestyle=':',arrowstyle='-|>,head_width=0.5,head_length=0.5', shrinkA=0, shrinkB=0, linewidth=2))\n",
    "f5ax3.axhline(df_average[\"pstar\"].where(df_average[\"jovern\"]==df_average[\"jovern\"].max()).max(),linestyle='--',color='firebrick')\n",
    "\n",
    "# Plot data-based constraint of gamma/lambda\n",
    "f5ax2.annotate(text='', xy=(np.max(data_goverl),np.min(f5ax2.get_ylim())), xytext=(np.min(data_goverl),np.min(f5ax2.get_ylim())), arrowprops=dict(color='black',arrowstyle='<|-|>,head_width=0.5,head_length=0.5', shrinkA=0, shrinkB=0, linewidth=2))\n",
    "\n",
    "# Co limitation zone\n",
    "f5ax3.fill_between((colimits[0],colimits[1]),\n",
    "                  np.min(f5ax3.get_ylim()), np.max(f5ax3.get_ylim()),color='#cccccc',zorder=-1)\n",
    "\n",
    "f5ax3.text(-7,1.25,'(c)',fontsize=16)\n",
    "f5ax3.text(8,1.09,'Iron replete\\n(macronutrient\\nlimited)',horizontalalignment='center',fontsize=14)\n",
    "f5ax3.text((colimits[1]+colimits[0])/2,1.09,'Co-\\nlimited',rotation=-90,horizontalalignment='center',fontsize=14)\n",
    "f5ax3.text(-2,1.09,'Iron limited\\n(macronutrient\\nreplete)',horizontalalignment='center',fontsize=14)\n",
    "\n",
    "# Plot data-based constraint of gamma/lambda\n",
    "f5ax3.annotate(text='', xy=(np.max(data_goverl),np.min(f5ax2.get_ylim())), xytext=(np.min(data_goverl),np.min(f5ax2.get_ylim())), arrowprops=dict(color='black',arrowstyle='<|-|>,head_width=0.5,head_length=0.5', shrinkA=0, shrinkB=0, linewidth=2))\n",
    "\n",
    "# The range for macronutrient usage efficiency values bounded by jovern>=jovern_fixed1nmlig is \n",
    "'''\n",
    "pstar_range=np.array((\\\n",
    "                df_average[\"pstar\"].where(np.logical_and(df_average[\"gaovla_average\"]<\n",
    "                                         df_average[\"gaovla_average\"].where(df_average[\"jovern\"]==df_average[\"jovern\"].max()).max(),\n",
    "                                         df_average[\"jovern\"]<=cntrl_output['jovern'].tail(1).values.max())).min()\n",
    "                df_average[\"pstar\"].where(np.logical_and(df_average[\"gaovla_average\"]>\n",
    "                                         df_average[\"gaovla_average\"].where(df_average[\"jovern\"]==df_average[\"jovern\"].max()).max(),\n",
    "                                         df_average[\"jovern\"]<=cntrl_output['jovern'].tail(1).values.max())).max()\n",
    "               ))\n",
    "'''\n",
    "# Can adjust the subplot size\n",
    "plt.subplots_adjust(wspace=0.4)\n",
    "\n",
    "plt.show()\n",
    "fig5.savefig('ensemble_model_data_plot.'+figfmt,format=figfmt,facecolor=fig5.get_facecolor(), edgecolor='none',bbox_inches='tight')\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We scored ensemble members by quantitative comparison to oceanic observations (S: possible range 0--1).\n",
    "Scores were compared to a benchmark evaluated from a model simulation where ligand concentration was held fixed at a uniform, global value of 1nM. Dynamic ligand simulations with model-data comparison scores greater than the benchmark value are outperforming the standard parameterization. The best scores are obtained in a band of intermediate gamma/lambda with moderate levels of macronutrients, iron, and ligands. \n",
    "\n",
    "Macronutrient use efficiency was diagnosed as the fraction of macronutrients transported from the surface to the deep by biological activity. Efficiency of macronutrient use is positively correlated with gamma/lambda, and reflects the pattern of export production in the low iron, upwelling \"Southern Ocean\" box."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}