{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Application showing the interpolation of a single species: _Biddulphia Sinensis_\n",
    "The interpolation of the full dataset (loop, plots and netCDF generation) is performed in [`interp_presence_absence_prod`](interp_presence_absence_prod.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "true"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using DIVAnd\n",
    "using PyPlot\n",
    "using Proj4\n",
    "using DelimitedFiles\n",
    "using PyCall\n",
    "using Dates\n",
    "using NCDatasets\n",
    "include(\"../scripts/PhytoInterp.jl\")\n",
    "doplot = true"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Files and directories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: File already downloaded\n",
      "└ @ Main In[2]:7\n"
     ]
    }
   ],
   "source": [
    "figdir = \"../product/figures/\"\n",
    "outputdir = \"../product/\"\n",
    "datadir = \"../data/\"\n",
    "datafile = joinpath(datadir, \"Biddulphia sinensis-1995-2020.csv\")\n",
    "isdir(datadir) ? \" \" : mkpath(datadir)\n",
    "isdir(figdir) ? \" \" : mkpath(figdir)\n",
    "isfile(datafile) ? @info(\"File already downloaded\") : download(\"https://dox.ulg.ac.be/index.php/s/VgLglubaTLetHzc/download\", datafile)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare mask\n",
    "### Interpolation grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51.0:0.1:56.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "longrid = -2.:0.1:10.\n",
    "latgrid = 51.:0.1:56."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"../data/gebco_30sec_4.nc\""
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bathname = joinpath(datadir, \"gebco_30sec_4.nc\")\n",
    "if !isfile(bathname)\n",
    "    download(\"https://dox.ulg.ac.be/index.php/s/RSwm4HPHImdZoQP/download\", bathname)\n",
    "else\n",
    "    @info(\"Bathymetry file already downloaded\")\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read bathymetry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "Figure(PyObject <Figure size 1920x1440 with 2 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "size(b) = (121, 51)\n"
     ]
    }
   ],
   "source": [
    "bx, by, b = load_bath(bathname, true, longrid, latgrid)\n",
    "@show size(b)\n",
    "\n",
    "if doplot\n",
    "    fig = PyPlot.figure()\n",
    "    ax = PyPlot.subplot(111)\n",
    "    pcolor(bx,by,b', vmin=0.); \n",
    "    colorbar(orientation=\"horizontal\")\n",
    "    title(\"Depth\")\n",
    "    show()\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask, (pm, pn),(xi, yi) = DIVAnd.DIVAnd_rectdom(longrid, latgrid);\n",
    "xi, yi, mask = DIVAnd.load_mask(bathname, true, longrid, latgrid, 0.0);\n",
    "xx, yy = ndgrid(xi, yi);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interpolation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Working on Biddulphia sinensis\n",
      "└ @ Main In[17]:3\n",
      "┌ Info: Presence: 6957, absence: 18820\n",
      "└ @ Main In[17]:15\n"
     ]
    }
   ],
   "source": [
    "speciesname = get_species_name(basename(datafile))\n",
    "speciesslug = get_species_slug(basename(datafile))\n",
    "@info(\"Working on $(speciesname)\")\n",
    "        \n",
    "# Data reading\n",
    "dates, lons, lats, occurs = read_data_phyto(datafile);\n",
    "\n",
    "# Conversion of coordinates\n",
    "lon, lat = transform_coords(lons, lats)\n",
    "\n",
    "data_presence = occurs .== 1;\n",
    "data_absence = .!(data_presence);\n",
    "npre = sum(data_presence)\n",
    "nabs = sum(data_absence);\n",
    "@info(\"Presence: $(npre), absence: $(nabs)\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute heatmap\n",
    "We compute: \n",
    "1. the heatmap using all the observation (presence or absence),\n",
    "2. the heatmap using only the presence observations,\n",
    "3. the heatmap only the absence observations.\n",
    "\n",
    "The first provides an indication of the sampling effort: high values in an area means a lot of measurements have been made in that area."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Warning: Data array size (25777,) is larger then maximum 10000. Superobservations will be created. To avoid, increase nmax to the desired number of superobs\n",
      "└ @ DIVAnd /home/ctroupin/.julia/packages/DIVAnd/ZfEqh/src/DIVAnd_heatmap.jl:48\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  1.142954 seconds (928.38 k allocations: 1.171 GiB, 4.46% gc time)\n",
      "  4.177570 seconds (621.36 k allocations: 4.755 GiB, 4.26% gc time)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Warning: Data array size (18820,) is larger then maximum 10000. Superobservations will be created. To avoid, increase nmax to the desired number of superobs\n",
      "└ @ DIVAnd /home/ctroupin/.julia/packages/DIVAnd/ZfEqh/src/DIVAnd_heatmap.jl:48\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0.974854 seconds (698.74 k allocations: 1.013 GiB, 3.90% gc time)\n"
     ]
    }
   ],
   "source": [
    "inflation = ones(length(lon));\n",
    "@time dens1, LHM1, LCV1, LSCV1 = DIVAnd_heatmap(mask, (pm,pn), (xx, yy), \n",
    "    (lon, lat), inflation, 0.2);\n",
    "@time dens2, LHM2, LCV2, LSCV2 = DIVAnd_heatmap(mask, (pm,pn), (xx, yy), \n",
    "    (lon[data_presence], lat[data_presence]), inflation[data_presence], 0.2);\n",
    "@time dens3, LHM3, LCV3, LSCV3 = DIVAnd_heatmap(mask, (pm,pn), (xx, yy), \n",
    "    (lon[data_absence], lat[data_absence]), inflation[data_absence], 0.2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, the probability field is computed as:\n",
    "```\n",
    "d = npre * dens2 / (npre * dens2 + nabs * dens3)\n",
    "```\n",
    "The reason for this equation is that the heatmap are computed so that their integral over the domain is 1, whatever the number of observations. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = npre .* dens2 ./ (npre .* dens2 .+ nabs .* dens3);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "UndefVarError",
     "evalue": "UndefVarError: d not defined",
     "output_type": "error",
     "traceback": [
      "UndefVarError: d not defined",
      "",
      "Stacktrace:",
      " [1] top-level scope at In[10]:2"
     ]
    }
   ],
   "source": [
    "if doplot\n",
    "    plot_heatmap(longrid, latgrid, d, lon, lat, occurs,\n",
    "        \"$(speciesname): normalised heatmap\")\n",
    "    pcolormesh(longrid, latgrid, mask', cmap=PyPlot.cm.binary_r)\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Write results in netCDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "UndefVarError",
     "evalue": "UndefVarError: d not defined",
     "output_type": "error",
     "traceback": [
      "UndefVarError: d not defined",
      "",
      "Stacktrace:",
      " [1] top-level scope at In[11]:1"
     ]
    }
   ],
   "source": [
    "create_nc_results(joinpath(outputdir, \"$(speciesname)_heatmap.nc\"), \n",
    "    longrid, latgrid, d, speciesname);"
   ]
  }
 ],
 "metadata": {
  "@webio": {
   "lastCommId": null,
   "lastKernelId": null
  },
  "kernelspec": {
   "display_name": "Julia 1.4.1",
   "language": "julia",
   "name": "julia-1.4"
  },
  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "1.4.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}