{
 "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_RCP26_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": [
    "# PISM_VUW = PISM_VUW\n",
    "# Read response functions\n",
    "\n",
    "fname = \"../RFunctions/RF_PISM_VUW_BM08_R1.dat\" # File to read\n",
    "with open(fname) as f:\n",
    "    RF_PISM_VUW_BM08_R1 = np.array([float(row) for row in f])\n",
    "\n",
    "fname = \"../RFunctions/RF_PISM_VUW_BM08_R2.dat\" # File to read\n",
    "with open(fname) as f:\n",
    "    RF_PISM_VUW_BM08_R2 = np.array([float(row) for row in f])\n",
    "\n",
    "fname = \"../RFunctions/RF_PISM_VUW_BM08_R3.dat\" # File to read\n",
    "with open(fname) as f:\n",
    "    RF_PISM_VUW_BM08_R3 = np.array([float(row) for row in f])\n",
    "\n",
    "fname = \"../RFunctions/RF_PISM_VUW_BM08_R4.dat\" # File to read\n",
    "with open(fname) as f:\n",
    "    RF_PISM_VUW_BM08_R4 = np.array([float(row) for row in f])\n",
    "\n",
    "fname = \"../RFunctions/RF_PISM_VUW_BM08_R5.dat\" # File to read\n",
    "with open(fname) as f:\n",
    "    RF_PISM_VUW_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": [
      "0.52635 0.5263157894736842\n",
      "0.8943 0.8947368421052632\n",
      "0.53045 0.5263157894736842\n",
      "0.47815 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_PISM_VUW_R1_RCP26 = [0] * (tend-tbeg)\n",
    "SL_wTd_nos_base_PISM_VUW_R2_RCP26 = [0] * (tend-tbeg)\n",
    "SL_wTd_nos_base_PISM_VUW_R3_RCP26 = [0] * (tend-tbeg)\n",
    "SL_wTd_nos_base_PISM_VUW_R4_RCP26 = [0] * (tend-tbeg)\n",
    "SL_wTd_nos_base_PISM_VUW_R5_RCP26 = [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_PISM_VUW_BM08_R1[t-tp]\n",
    "        SL.append(dSL)\n",
    "    SL_wTd_nos_base_PISM_VUW_R1_RCP26=np.vstack([SL_wTd_nos_base_PISM_VUW_R1_RCP26, 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_PISM_VUW_BM08_R2[t-tp]\n",
    "        SL.append(dSL)\n",
    "    SL_wTd_nos_base_PISM_VUW_R2_RCP26=np.vstack([SL_wTd_nos_base_PISM_VUW_R2_RCP26, 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_PISM_VUW_BM08_R3[t-tp]\n",
    "        SL.append(dSL)\n",
    "    SL_wTd_nos_base_PISM_VUW_R3_RCP26=np.vstack([SL_wTd_nos_base_PISM_VUW_R3_RCP26, 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_PISM_VUW_BM08_R4[t-tp]\n",
    "        SL.append(dSL)\n",
    "    SL_wTd_nos_base_PISM_VUW_R4_RCP26=np.vstack([SL_wTd_nos_base_PISM_VUW_R4_RCP26, 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_PISM_VUW_BM08_R5[t-tp]\n",
    "        SL.append(dSL)\n",
    "    SL_wTd_nos_base_PISM_VUW_R5_RCP26=np.vstack([SL_wTd_nos_base_PISM_VUW_R5_RCP26, SL])\n",
    "\n",
    "SL_wTd_nos_base_PISM_VUW_SU_RCP26 = SL_wTd_nos_base_PISM_VUW_R1_RCP26+SL_wTd_nos_base_PISM_VUW_R2_RCP26+SL_wTd_nos_base_PISM_VUW_R3_RCP26+SL_wTd_nos_base_PISM_VUW_R4_RCP26+SL_wTd_nos_base_PISM_VUW_R5_RCP26\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_PISM_VUW_RCP26.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_PISM_VUW_R1_RCP26\n",
    "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_PISM_VUW_R2_RCP26\n",
    "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_PISM_VUW_R3_RCP26\n",
    "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_PISM_VUW_R4_RCP26\n",
    "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_PISM_VUW_R5_RCP26\n",
    "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_PISM_VUW_SU_RCP26\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_PISM_VUW_SU_RCP26[i,:])\n",
    "\n",
    "plt.show()\n",
    "fp3.savefig(\"Figures/SL_wTd_nos_base_PISM_VUW_SU_RCP26_alllines.pdf\", bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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