{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import netCDF4 as nc\n",
    "import matplotlib.pylab as plt\n",
    "import imp\n",
    "import csv\n",
    "import pandas as pd\n",
    "import random as rnd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "651\n"
     ]
    }
   ],
   "source": [
    "# Read atmospheric forcing\n",
    "\n",
    "NumTensemble = 600\n",
    "Tlen = 651\n",
    "\n",
    "fname = \"../MAGICC/CMIP5_RCP2500/Temp_CMIP5_RCP85_HistConstraint.dat\" # File to read\n",
    "df = pd.read_csv(fname,sep='\\s+',index_col=0,header=None)\n",
    "df.columns.name = \"ensemble member\"\n",
    "df.index.name = \"Time\"\n",
    "SAT = np.array(df.values)\n",
    "\n",
    "print(len(SAT[:,1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize and crop temperature series\n",
    "Temp = []\n",
    "Tavebeg = 0\n",
    "Taveend = 80\n",
    "tbeg = 51\n",
    "tend = 251\n",
    "for i in range(len(SAT[1,:])):\n",
    "    SATave = np.mean(SAT[Tavebeg:Taveend,i])\n",
    "    SAT[:,i] = SAT[:,i]-SATave\n",
    "SAT = SAT[tbeg:tend,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read ocean scaling\n",
    "\n",
    "NumOmodel = 19\n",
    "\n",
    "fname = \"../ScalingCoefficients/OceanScaling/OS_R1.dat\" # File to read\n",
    "with open(fname) as f:\n",
    "    OS_NoDelay_R1 = np.array([float(row.split(\"\\t\")[0]) for row in f])\n",
    "with open(fname) as f:\n",
    "    OS_WiDelay_R1 = np.array([float(row.split(\"\\t\")[3]) for row in f])\n",
    "with open(fname) as f:\n",
    "    OS_Delay_R1 = np.array([float(row.split(\"\\t\")[2]) for row in f])\n",
    "\n",
    "fname = \"../ScalingCoefficients/OceanScaling/OS_R2.dat\" # File to read\n",
    "with open(fname) as f:\n",
    "    OS_NoDelay_R2 = np.array([float(row.split(\"\\t\")[0]) for row in f])\n",
    "with open(fname) as f:\n",
    "    OS_WiDelay_R2 = np.array([float(row.split(\"\\t\")[3]) for row in f])\n",
    "with open(fname) as f:\n",
    "    OS_Delay_R2 = np.array([float(row.split(\"\\t\")[2]) for row in f])\n",
    "\n",
    "fname = \"../ScalingCoefficients/OceanScaling/OS_R3.dat\" # File to read\n",
    "with open(fname) as f:\n",
    "    OS_NoDelay_R3 = np.array([float(row.split(\"\\t\")[0]) for row in f])\n",
    "with open(fname) as f:\n",
    "    OS_WiDelay_R3 = np.array([float(row.split(\"\\t\")[3]) for row in f])\n",
    "with open(fname) as f:\n",
    "    OS_Delay_R3 = np.array([float(row.split(\"\\t\")[2]) for row in f])\n",
    "\n",
    "fname = \"../ScalingCoefficients/OceanScaling/OS_R4.dat\" # File to read\n",
    "with open(fname) as f:\n",
    "    OS_NoDelay_R4 = np.array([float(row.split(\"\\t\")[0]) for row in f])\n",
    "with open(fname) as f:\n",
    "    OS_WiDelay_R4 = np.array([float(row.split(\"\\t\")[3]) for row in f])\n",
    "with open(fname) as f:\n",
    "    OS_Delay_R4 = np.array([float(row.split(\"\\t\")[2]) for row in f])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read melting sensitivity\n",
    "fname = \"../ScalingCoefficients/MeltSensitivity/MeltSensitivity.dat\" # File to read\n",
    "with open(fname) as f:\n",
    "    MeltSensitivity = np.array([float(row) for row in f])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# UA_UNN = UA_UNN\n",
    "# Read response functions\n",
    "\n",
    "fname = \"../RFunctions/RF_UA_UNN_BM08_R1.dat\" # File to read\n",
    "with open(fname) as f:\n",
    "    RF_UA_UNN_BM08_R1 = np.array([float(row) for row in f])\n",
    "\n",
    "fname = \"../RFunctions/RF_UA_UNN_BM08_R2.dat\" # File to read\n",
    "with open(fname) as f:\n",
    "    RF_UA_UNN_BM08_R2 = np.array([float(row) for row in f])\n",
    "\n",
    "fname = \"../RFunctions/RF_UA_UNN_BM08_R3.dat\" # File to read\n",
    "with open(fname) as f:\n",
    "    RF_UA_UNN_BM08_R3 = np.array([float(row) for row in f])\n",
    "\n",
    "fname = \"../RFunctions/RF_UA_UNN_BM08_R4.dat\" # File to read\n",
    "with open(fname) as f:\n",
    "    RF_UA_UNN_BM08_R4 = np.array([float(row) for row in f])\n",
    "\n",
    "fname = \"../RFunctions/RF_UA_UNN_BM08_R5.dat\" # File to read\n",
    "with open(fname) as f:\n",
    "    RF_UA_UNN_BM08_R5 = np.array([float(row) for row in f])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5253 0.5263157894736842\n",
      "0.89375 0.8947368421052632\n",
      "0.5372 0.5263157894736842\n",
      "0.4768 0.47368421052631576\n"
     ]
    }
   ],
   "source": [
    "EnsembleSize = 20000\n",
    "scaled_forcing = False\n",
    "\n",
    "countR1 = 0\n",
    "countR2 = 0\n",
    "countR3 = 0\n",
    "countR4 = 0\n",
    "\n",
    "SL_wTd_nos_base_UA_UNN_R1_RCP85 = [0] * (tend-tbeg)\n",
    "SL_wTd_nos_base_UA_UNN_R2_RCP85 = [0] * (tend-tbeg)\n",
    "SL_wTd_nos_base_UA_UNN_R3_RCP85 = [0] * (tend-tbeg)\n",
    "SL_wTd_nos_base_UA_UNN_R4_RCP85 = [0] * (tend-tbeg)\n",
    "SL_wTd_nos_base_UA_UNN_R5_RCP85 = [0] * (tend-tbeg)\n",
    "\n",
    "for i in range(EnsembleSize):\n",
    "\n",
    "    # Select forcing randomly\n",
    "\n",
    "    # select global warming path\n",
    "    iTens = rnd.randint(0,NumTensemble-1)\n",
    "    Temp = np.array(SAT[:,iTens])\n",
    "\n",
    "    # select ocean model\n",
    "    iOmod = rnd.randint(0,NumOmodel-1)\n",
    "    OS_R1 = OS_WiDelay_R1[iOmod]\n",
    "    OS_R2 = OS_WiDelay_R2[iOmod]\n",
    "    OS_R3 = OS_WiDelay_R3[iOmod]\n",
    "    OS_R4 = OS_WiDelay_R4[iOmod]\n",
    "    OS_R5 = OS_WiDelay_R4[iOmod]\n",
    "\n",
    "    tau_R1 = int(OS_Delay_R1[iOmod])\n",
    "    tau_R2 = int(OS_Delay_R2[iOmod])\n",
    "    tau_R3 = int(OS_Delay_R3[iOmod])\n",
    "    tau_R4 = int(OS_Delay_R4[iOmod])\n",
    "    tau_R5 = int(OS_Delay_R4[iOmod])\n",
    "\n",
    "    if tau_R1>0:\n",
    "        countR1 = countR1+1\n",
    "    if tau_R2>0:\n",
    "        countR2 = countR2+1\n",
    "    if tau_R3>0:\n",
    "        countR3 = countR3+1\n",
    "    if tau_R4>0:\n",
    "        countR4 = countR4+1\n",
    "    \n",
    "    Temp_R1 = np.append(np.zeros(tau_R1),Temp[:tend-tbeg-tau_R1])\n",
    "    Temp_R2 = np.append(np.zeros(tau_R2),Temp[:tend-tbeg-tau_R2])\n",
    "    Temp_R3 = np.append(np.zeros(tau_R3),Temp[:tend-tbeg-tau_R3])\n",
    "    Temp_R4 = np.append(np.zeros(tau_R4),Temp[:tend-tbeg-tau_R4])\n",
    "    Temp_R5 = np.append(np.zeros(tau_R5),Temp[:tend-tbeg-tau_R5])\n",
    "    \n",
    "    # select melting sensitivity\n",
    "    MS_R1 = rnd.uniform(MeltSensitivity[0],MeltSensitivity[1])\n",
    "    MS_R2 = rnd.uniform(MeltSensitivity[0],MeltSensitivity[1])\n",
    "    MS_R3 = rnd.uniform(MeltSensitivity[0],MeltSensitivity[1])\n",
    "    MS_R4 = rnd.uniform(MeltSensitivity[0],MeltSensitivity[1])\n",
    "    MS_R5 = rnd.uniform(MeltSensitivity[0],MeltSensitivity[1])\n",
    "\n",
    "    # Compose forcing time series\n",
    "    M_R1 = MS_R1*OS_R1*Temp_R1\n",
    "    M_R2 = MS_R2*OS_R2*Temp_R2\n",
    "    M_R3 = MS_R3*OS_R3*Temp_R3\n",
    "    M_R4 = MS_R4*OS_R4*Temp_R4\n",
    "    M_R5 = MS_R5*OS_R5*Temp_R5\n",
    "\n",
    "    M_R1[M_R1 < 0.0] = 0.0\n",
    "    M_R2[M_R2 < 0.0] = 0.0\n",
    "    M_R3[M_R3 < 0.0] = 0.0\n",
    "    M_R4[M_R4 < 0.0] = 0.0\n",
    "    M_R5[M_R5 < 0.0] = 0.0\n",
    "    \n",
    "    # Linear response\n",
    "    SL = []\n",
    "    SL.append(0)\n",
    "    for t in range(1,tend-tbeg):\n",
    "        dSL = 0\n",
    "        for tp in range(0,t):\n",
    "            dSL = dSL + M_R1[tp]*RF_UA_UNN_BM08_R1[t-tp]\n",
    "        SL.append(dSL)\n",
    "    SL_wTd_nos_base_UA_UNN_R1_RCP85=np.vstack([SL_wTd_nos_base_UA_UNN_R1_RCP85, SL])\n",
    "\n",
    "    SL = []\n",
    "    SL.append(0)\n",
    "    for t in range(1,tend-tbeg):\n",
    "        dSL = 0\n",
    "        for tp in range(0,t):\n",
    "            dSL = dSL + M_R2[tp]*RF_UA_UNN_BM08_R2[t-tp]\n",
    "        SL.append(dSL)\n",
    "    SL_wTd_nos_base_UA_UNN_R2_RCP85=np.vstack([SL_wTd_nos_base_UA_UNN_R2_RCP85, SL])\n",
    "\n",
    "    SL = []\n",
    "    SL.append(0)\n",
    "    for t in range(1,tend-tbeg):\n",
    "        dSL = 0\n",
    "        for tp in range(0,t):\n",
    "            dSL = dSL + M_R3[tp]*RF_UA_UNN_BM08_R3[t-tp]\n",
    "        SL.append(dSL)\n",
    "    SL_wTd_nos_base_UA_UNN_R3_RCP85=np.vstack([SL_wTd_nos_base_UA_UNN_R3_RCP85, SL])\n",
    "\n",
    "    SL = []\n",
    "    SL.append(0)\n",
    "    for t in range(1,tend-tbeg):\n",
    "        dSL = 0\n",
    "        for tp in range(0,t):\n",
    "            dSL = dSL + M_R4[tp]*RF_UA_UNN_BM08_R4[t-tp]\n",
    "        SL.append(dSL)\n",
    "    SL_wTd_nos_base_UA_UNN_R4_RCP85=np.vstack([SL_wTd_nos_base_UA_UNN_R4_RCP85, SL])\n",
    "\n",
    "    SL = []\n",
    "    SL.append(0)\n",
    "    for t in range(1,tend-tbeg):\n",
    "        dSL = 0\n",
    "        for tp in range(0,t):\n",
    "            dSL = dSL + M_R5[tp]*RF_UA_UNN_BM08_R5[t-tp]\n",
    "        SL.append(dSL)\n",
    "    SL_wTd_nos_base_UA_UNN_R5_RCP85=np.vstack([SL_wTd_nos_base_UA_UNN_R5_RCP85, SL])\n",
    "\n",
    "SL_wTd_nos_base_UA_UNN_SU_RCP85 = SL_wTd_nos_base_UA_UNN_R1_RCP85+SL_wTd_nos_base_UA_UNN_R2_RCP85+SL_wTd_nos_base_UA_UNN_R3_RCP85+SL_wTd_nos_base_UA_UNN_R4_RCP85+SL_wTd_nos_base_UA_UNN_R5_RCP85\n",
    "\n",
    "print(countR1/EnsembleSize,10/19)\n",
    "print(countR2/EnsembleSize,17/19)\n",
    "print(countR3/EnsembleSize,10/19)\n",
    "print(countR4/EnsembleSize,9/19)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "Time = range(1900,2100)\n",
    "ncfile = nc.Dataset('EnsembleSingleModelProjections/SL_wTd_nos_base_UA_UNN_RCP85.nc','w', format='NETCDF4')\n",
    "ncfile.createDimension('Time', None)\n",
    "ncfile.createDimension('Emember', None)\n",
    "\n",
    "SL_wTd_nos_base_R1 = ncfile.createVariable('EAIS', 'f4', ('Emember', 'Time'))\n",
    "SL_wTd_nos_base_R2 = ncfile.createVariable('Ross', 'f4', ('Emember', 'Time'))\n",
    "SL_wTd_nos_base_R3 = ncfile.createVariable('Amundsen', 'f4', ('Emember', 'Time'))\n",
    "SL_wTd_nos_base_R4 = ncfile.createVariable('Weddell', 'f4', ('Emember', 'Time'))\n",
    "SL_wTd_nos_base_R5 = ncfile.createVariable('Peninsula', 'f4', ('Emember', 'Time'))\n",
    "SL_wTd_nos_base_SU = ncfile.createVariable('Antarctica', 'f4', ('Emember', 'Time'))\n",
    "t = ncfile.createVariable('Time', 'i4', 'Time')\n",
    "\n",
    "SL_wTd_nos_base_R1[:,:] = SL_wTd_nos_base_UA_UNN_R1_RCP85\n",
    "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_UA_UNN_R2_RCP85\n",
    "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_UA_UNN_R3_RCP85\n",
    "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_UA_UNN_R4_RCP85\n",
    "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_UA_UNN_R5_RCP85\n",
    "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_UA_UNN_SU_RCP85\n",
    "t[:] = Time\n",
    "\n",
    "SL_wTd_nos_base_R1.units = 'meter'\n",
    "SL_wTd_nos_base_R2.units = 'meter'\n",
    "SL_wTd_nos_base_R3.units = 'meter'\n",
    "SL_wTd_nos_base_R4.units = 'meter'\n",
    "SL_wTd_nos_base_R5.units = 'meter'\n",
    "SL_wTd_nos_base_SU.units = 'meter'\n",
    "\n",
    "t.units = 'years'\n",
    "\n",
    "ncfile.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fp3=plt.figure()\n",
    "for i in range(EnsembleSize):\n",
    "    plt.plot(Time,SL_wTd_nos_base_UA_UNN_SU_RCP85[i,:])\n",
    "\n",
    "plt.show()\n",
    "fp3.savefig(\"Figures/SL_wTd_nos_base_UA_UNN_SU_RCP85_alllines.pdf\", bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}