{
 "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": [
    "# CISM_NCA = CISM_NCA\n",
    "# Read response functions\n",
    "\n",
    "fname = \"../RFunctions/RF_CISM_NCA_BM08_R1.dat\" # File to read\n",
    "with open(fname) as f:\n",
    "    RF_CISM_NCA_BM08_R1 = np.array([float(row) for row in f])\n",
    "\n",
    "fname = \"../RFunctions/RF_CISM_NCA_BM08_R2.dat\" # File to read\n",
    "with open(fname) as f:\n",
    "    RF_CISM_NCA_BM08_R2 = np.array([float(row) for row in f])\n",
    "\n",
    "fname = \"../RFunctions/RF_CISM_NCA_BM08_R3.dat\" # File to read\n",
    "with open(fname) as f:\n",
    "    RF_CISM_NCA_BM08_R3 = np.array([float(row) for row in f])\n",
    "\n",
    "fname = \"../RFunctions/RF_CISM_NCA_BM08_R4.dat\" # File to read\n",
    "with open(fname) as f:\n",
    "    RF_CISM_NCA_BM08_R4 = np.array([float(row) for row in f])\n",
    "\n",
    "fname = \"../RFunctions/RF_CISM_NCA_BM08_R5.dat\" # File to read\n",
    "with open(fname) as f:\n",
    "    RF_CISM_NCA_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": [
      "0.5283 0.5263157894736842\n",
      "0.8969 0.8947368421052632\n",
      "0.52565 0.5263157894736842\n",
      "0.47485 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_CISM_NCA_R1_RCP26 = [0] * (tend-tbeg)\n",
    "SL_wTd_nos_base_CISM_NCA_R2_RCP26 = [0] * (tend-tbeg)\n",
    "SL_wTd_nos_base_CISM_NCA_R3_RCP26 = [0] * (tend-tbeg)\n",
    "SL_wTd_nos_base_CISM_NCA_R4_RCP26 = [0] * (tend-tbeg)\n",
    "SL_wTd_nos_base_CISM_NCA_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_CISM_NCA_BM08_R1[t-tp]\n",
    "        SL.append(dSL)\n",
    "    SL_wTd_nos_base_CISM_NCA_R1_RCP26=np.vstack([SL_wTd_nos_base_CISM_NCA_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_CISM_NCA_BM08_R2[t-tp]\n",
    "        SL.append(dSL)\n",
    "    SL_wTd_nos_base_CISM_NCA_R2_RCP26=np.vstack([SL_wTd_nos_base_CISM_NCA_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_CISM_NCA_BM08_R3[t-tp]\n",
    "        SL.append(dSL)\n",
    "    SL_wTd_nos_base_CISM_NCA_R3_RCP26=np.vstack([SL_wTd_nos_base_CISM_NCA_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_CISM_NCA_BM08_R4[t-tp]\n",
    "        SL.append(dSL)\n",
    "    SL_wTd_nos_base_CISM_NCA_R4_RCP26=np.vstack([SL_wTd_nos_base_CISM_NCA_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_CISM_NCA_BM08_R5[t-tp]\n",
    "        SL.append(dSL)\n",
    "    SL_wTd_nos_base_CISM_NCA_R5_RCP26=np.vstack([SL_wTd_nos_base_CISM_NCA_R5_RCP26, SL])\n",
    "\n",
    "SL_wTd_nos_base_CISM_NCA_SU_RCP26 = SL_wTd_nos_base_CISM_NCA_R1_RCP26+SL_wTd_nos_base_CISM_NCA_R2_RCP26+SL_wTd_nos_base_CISM_NCA_R3_RCP26+SL_wTd_nos_base_CISM_NCA_R4_RCP26+SL_wTd_nos_base_CISM_NCA_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_CISM_NCA_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_CISM_NCA_R1_RCP26\n",
    "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_CISM_NCA_R2_RCP26\n",
    "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_CISM_NCA_R3_RCP26\n",
    "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_CISM_NCA_R4_RCP26\n",
    "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_CISM_NCA_R5_RCP26\n",
    "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_CISM_NCA_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_CISM_NCA_SU_RCP26[i,:])\n",
    "\n",
    "plt.show()\n",
    "fp3.savefig(\"Figures/SL_wTd_nos_base_CISM_NCA_SU_RCP26_alllines.pdf\", bbox_inches='tight')\n"
   ]
  },
  {
   "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
}