{
 "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_DMI = PISM_DMI\n",
    "# Read response functions\n",
    "\n",
    "fname = \"../RFunctions/RF_PISM_DMI_BM08_R1.dat\" # File to read\n",
    "with open(fname) as f:\n",
    "    RF_PISM_DMI_BM08_R1 = np.array([float(row) for row in f])\n",
    "\n",
    "fname = \"../RFunctions/RF_PISM_DMI_BM08_R2.dat\" # File to read\n",
    "with open(fname) as f:\n",
    "    RF_PISM_DMI_BM08_R2 = np.array([float(row) for row in f])\n",
    "\n",
    "fname = \"../RFunctions/RF_PISM_DMI_BM08_R3.dat\" # File to read\n",
    "with open(fname) as f:\n",
    "    RF_PISM_DMI_BM08_R3 = np.array([float(row) for row in f])\n",
    "\n",
    "fname = \"../RFunctions/RF_PISM_DMI_BM08_R4.dat\" # File to read\n",
    "with open(fname) as f:\n",
    "    RF_PISM_DMI_BM08_R4 = np.array([float(row) for row in f])\n",
    "\n",
    "fname = \"../RFunctions/RF_PISM_DMI_BM08_R5.dat\" # File to read\n",
    "with open(fname) as f:\n",
    "    RF_PISM_DMI_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": [
      "0.52705 0.5263157894736842\n",
      "0.8944 0.8947368421052632\n",
      "0.5235 0.5263157894736842\n",
      "0.47615 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_DMI_R1_RCP26 = [0] * (tend-tbeg)\n",
    "SL_wTd_nos_base_PISM_DMI_R2_RCP26 = [0] * (tend-tbeg)\n",
    "SL_wTd_nos_base_PISM_DMI_R3_RCP26 = [0] * (tend-tbeg)\n",
    "SL_wTd_nos_base_PISM_DMI_R4_RCP26 = [0] * (tend-tbeg)\n",
    "SL_wTd_nos_base_PISM_DMI_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_DMI_BM08_R1[t-tp]\n",
    "        SL.append(dSL)\n",
    "    SL_wTd_nos_base_PISM_DMI_R1_RCP26=np.vstack([SL_wTd_nos_base_PISM_DMI_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_DMI_BM08_R2[t-tp]\n",
    "        SL.append(dSL)\n",
    "    SL_wTd_nos_base_PISM_DMI_R2_RCP26=np.vstack([SL_wTd_nos_base_PISM_DMI_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_DMI_BM08_R3[t-tp]\n",
    "        SL.append(dSL)\n",
    "    SL_wTd_nos_base_PISM_DMI_R3_RCP26=np.vstack([SL_wTd_nos_base_PISM_DMI_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_DMI_BM08_R4[t-tp]\n",
    "        SL.append(dSL)\n",
    "    SL_wTd_nos_base_PISM_DMI_R4_RCP26=np.vstack([SL_wTd_nos_base_PISM_DMI_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_DMI_BM08_R5[t-tp]\n",
    "        SL.append(dSL)\n",
    "    SL_wTd_nos_base_PISM_DMI_R5_RCP26=np.vstack([SL_wTd_nos_base_PISM_DMI_R5_RCP26, SL])\n",
    "\n",
    "SL_wTd_nos_base_PISM_DMI_SU_RCP26 = SL_wTd_nos_base_PISM_DMI_R1_RCP26+SL_wTd_nos_base_PISM_DMI_R2_RCP26+SL_wTd_nos_base_PISM_DMI_R3_RCP26+SL_wTd_nos_base_PISM_DMI_R4_RCP26+SL_wTd_nos_base_PISM_DMI_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_DMI_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_DMI_R1_RCP26\n",
    "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_PISM_DMI_R2_RCP26\n",
    "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_PISM_DMI_R3_RCP26\n",
    "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_PISM_DMI_R4_RCP26\n",
    "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_PISM_DMI_R5_RCP26\n",
    "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_PISM_DMI_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": {
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     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fp3=plt.figure()\n",
    "for i in range(EnsembleSize):\n",
    "    plt.plot(Time,SL_wTd_nos_base_PISM_DMI_SU_RCP26[i,:])\n",
    "\n",
    "plt.show()\n",
    "fp3.savefig(\"Figures/SL_wTd_nos_base_PISM_DMI_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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