{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Based on Elise Olson's https://github.com/SalishSeaCast/analysis-elise-2/blob/master/notebooks/modelEqs/NewRateComparison.ipynb notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import netCDF4 as nc\n",
    "import matplotlib.pyplot as plt\n",
    "from salishsea_tools import bio_tools as bt, places\n",
    "import xarray as xr\n",
    "import os\n",
    "import glob\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "nml=bt.load_nml_bio(resDir='/ocean/eolson/MEOPAR/NEMO-3.6-code/NEMOGCM/CONFIG/SalishSeaCast/EXP00/',\n",
    "                 nmlname='nampisprod',bioRefName='namelist_smelt_cfg_HC201905equiv',bioCfgName='namelist_smelt_cfg_HC201905equiv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Namelist([('zz_rate_r_diat', 6.0495e-05),\n",
       "          ('zz_rate_r_myri', 2.22e-05),\n",
       "          ('zz_rate_r_nano', 2.109e-05),\n",
       "          ('zz_rate_maxtemp_diat', 26.0),\n",
       "          ('zz_rate_maxtemp_myri', 31.0),\n",
       "          ('zz_rate_maxtemp_nano', 31.0),\n",
       "          ('zz_rate_temprange_diat', 14.0),\n",
       "          ('zz_rate_temprange_myri', 13.0),\n",
       "          ('zz_rate_temprange_nano', 13.0),\n",
       "          ('zz_rate_iopt_diat', 45.0),\n",
       "          ('zz_rate_iopt_myri', 37.0),\n",
       "          ('zz_rate_iopt_nano', 10.0),\n",
       "          ('zz_rate_gamma_diat', 0.0),\n",
       "          ('zz_rate_gamma_myri', 0.0),\n",
       "          ('zz_rate_gamma_nano', 0.0),\n",
       "          ('zz_rate_k_si_diat', 2.2),\n",
       "          ('zz_rate_k_si_myri', 0.0),\n",
       "          ('zz_rate_k_si_nano', 0.0),\n",
       "          ('zz_rate_kapa_diat', 1.0),\n",
       "          ('zz_rate_kapa_myri', 0.5),\n",
       "          ('zz_rate_kapa_nano', 0.3),\n",
       "          ('zz_rate_k_diat', 2.0),\n",
       "          ('zz_rate_k_myri', 0.5),\n",
       "          ('zz_rate_k_nano', 0.2),\n",
       "          ('zz_rate_si_ratio_diat', 1.8),\n",
       "          ('zz_rate_si_ratio_myri', 0.0),\n",
       "          ('zz_rate_si_ratio_nano', 0.0)])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "nml2=bt.load_nml_bio(resDir='/data/sallen/results/MEOPAR/Karyn/01jan08_tsc/',\n",
    "                 nmlname='nampisprod',bioRefName='namelist_smelt_cfg',bioCfgName='namelist_smelt_cfg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def phyto_Tdep_Factor(TT, zz_rate_maxtemp, zz_rate_temprange):\n",
    "    if hasattr(TT,'__len__'): # assume 1-d array or similar and return array\n",
    "        return np.array([phyto_Tdep_Factor(el,zz_rate_maxtemp, zz_rate_temprange) for el in TT])\n",
    "    else:\n",
    "        return np.exp(0.07 * (TT - 20)) * min(max((zz_rate_maxtemp - TT), 0.0),zz_rate_temprange) / (zz_rate_temprange + 1e-10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_T_Factors(TT,nampisprod):\n",
    "    Tdep_Diat=phyto_Tdep_Factor(TT,nampisprod['zz_rate_maxtemp_diat'],nampisprod['zz_rate_temprange_diat'])\n",
    "    #Tdep_DiatEXP=phyto_Tdep_FactorEXP(TT,nampisprod['zz_rate_maxtemp_diat'],nampisprod['zz_rate_temprange_diat'])\n",
    "    Tdep_Nano=phyto_Tdep_Factor(TT,nampisprod['zz_rate_maxtemp_nano'],nampisprod['zz_rate_temprange_nano'])\n",
    "    return Tdep_Diat,  Tdep_Nano"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "t=np.linspace(0,35,60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "TdepDExp=calc_T_Factors(t,nml2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.24659696, 0.25705259, 0.26795154, 0.27931259, 0.29115535,\n",
       "        0.30350024, 0.31636855, 0.32978247, 0.34376513, 0.35834066,\n",
       "        0.37353418, 0.3893719 , 0.40588114, 0.42309037, 0.44102926,\n",
       "        0.45972875, 0.47922109, 0.4995399 , 0.52072023, 0.54279859,\n",
       "        0.56581307, 0.58980335, 0.61481081, 0.64087859, 0.66805162,\n",
       "        0.69637679, 0.7039325 , 0.70171625, 0.69804658, 0.69280422,\n",
       "        0.68586242, 0.67708648, 0.66633336, 0.65345115, 0.63827866,\n",
       "        0.62064481, 0.60036818, 0.57725635, 0.55110535, 0.52169901,\n",
       "        0.48880829, 0.45219059, 0.41158898, 0.36673147, 0.31733015,\n",
       "        0.26308034, 0.20365971, 0.1387273 , 0.06792255, 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ]),\n",
       " array([0.24659696, 0.25705259, 0.26795154, 0.27931259, 0.29115535,\n",
       "        0.30350024, 0.31636855, 0.32978247, 0.34376513, 0.35834066,\n",
       "        0.37353418, 0.3893719 , 0.40588114, 0.42309037, 0.44102926,\n",
       "        0.45972875, 0.47922109, 0.4995399 , 0.52072023, 0.54279859,\n",
       "        0.56581307, 0.58980335, 0.61481081, 0.64087859, 0.66805162,\n",
       "        0.69637679, 0.72590293, 0.75668097, 0.78876398, 0.82220731,\n",
       "        0.85706862, 0.86661745, 0.86086488, 0.85306656, 0.84305933,\n",
       "        0.83066992, 0.8157143 , 0.79799712, 0.77731111, 0.75343636,\n",
       "        0.72613965, 0.69517371, 0.66027648, 0.62117026, 0.57756085,\n",
       "        0.5291367 , 0.47556791, 0.41650528, 0.35157924, 0.28039875,\n",
       "        0.20255015, 0.11759596, 0.02507359, 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ]))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TdepDExp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "TdepD,TdepF=calc_T_Factors(t,nml)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.24659696, 0.25705259, 0.26795154, 0.27931259, 0.29115535,\n",
       "       0.30350024, 0.31636855, 0.32978247, 0.34376513, 0.35834066,\n",
       "       0.37353418, 0.3893719 , 0.40588114, 0.42309037, 0.44102926,\n",
       "       0.45972875, 0.47922109, 0.4995399 , 0.52072023, 0.54279859,\n",
       "       0.56581307, 0.57052406, 0.56866279, 0.56561803, 0.56129277,\n",
       "       0.5555839 , 0.54838187, 0.53957033, 0.52902572, 0.51661694,\n",
       "       0.50220486, 0.4856419 , 0.46677159, 0.44542803, 0.42143543,\n",
       "       0.39460751, 0.36474696, 0.33164487, 0.29508003, 0.2548183 ,\n",
       "       0.21061193, 0.1621988 , 0.10930164, 0.05162725, 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TdepD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(figsize=(5,4))\n",
    "\n",
    "\n",
    "\n",
    "plt.plot(t,TdepD,'b-',label='Diatoms')\n",
    "plt.plot(t,TdepDExp[0],'b--',label='Diatoms Exp 3 & 4')\n",
    "plt.plot(t,TdepF,'r-',label='Nanoflagellates')\n",
    "#plt.title('Temperature response of diatoms and flagellates')\n",
    "\n",
    "ax.set_ylabel('Temperature-dependent Growth Rate')\n",
    "ax.set_xlabel('Temperature (degrees Celsius)')\n",
    "ax.legend(frameon=False)\n",
    "\n",
    "plt.savefig('SuppFigureS3_TempOptima.png', bbox_inches='tight',dpi=1000,transparent=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (py39)",
   "language": "python",
   "name": "py39"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.15"
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 "nbformat": 4,
 "nbformat_minor": 4
}