{
 "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]))\n",
    "# SAT[time,ensemblemember]"
   ]
  },
  {
   "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])"
   ]
  },
  {
   "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 ice scaling\n",
    "fname = \"../ScalingCoefficients/IceScaling/ES_CISM_NCA_scaledto08.dat\" # File to read\n",
    "with open(fname) as f:\n",
    "    FS_CISM_NCA_BM08 = np.array([float(row) for row in f])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5275 0.5263157894736842\n",
      "0.89895 0.8947368421052632\n",
      "0.5217 0.5263157894736842\n",
      "0.47455 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_RCP85 = [0] * (tend-tbeg)\n",
    "SL_wTd_nos_base_CISM_NCA_R2_RCP85 = [0] * (tend-tbeg)\n",
    "SL_wTd_nos_base_CISM_NCA_R3_RCP85 = [0] * (tend-tbeg)\n",
    "SL_wTd_nos_base_CISM_NCA_R4_RCP85 = [0] * (tend-tbeg)\n",
    "SL_wTd_nos_base_CISM_NCA_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",
    "    # Scaling of forcing\n",
    "    if (scaled_forcing == True):\n",
    "        for i in range(len(M_R1)):\n",
    "            if M_R1[i] > 0.0:\n",
    "                dump = np.log(M_R1[i]/8)/np.log(2.0)\n",
    "                M_R1[i] = M_R1[i] * FS_CISM_NCA_BM08[1]**dump\n",
    "            if M_R2[i] > 0.0:\n",
    "                dump = np.log(M_R2[i]/8)/np.log(2.0)\n",
    "                M_R2[i] = M_R2[i] * FS_CISM_NCA_BM08[2]**dump\n",
    "            if M_R3[i] > 0.0:\n",
    "                dump = np.log(M_R3[i]/8)/np.log(2.0)\n",
    "                M_R3[i] = M_R3[i] * FS_CISM_NCA_BM08[3]**dump\n",
    "            if M_R4[i] > 0.0:\n",
    "                dump = np.log(M_R4[i]/8)/np.log(2.0)\n",
    "                M_R4[i] = M_R4[i] * FS_CISM_NCA_BM08[4]**dump\n",
    "            if M_R5[i] > 0.0:\n",
    "                dump = np.log(M_R5[i]/8)/np.log(2.0)\n",
    "                M_R5[i] = M_R5[i] * FS_CISM_NCA_BM08[5]**dump\n",
    "\n",
    "    # Linear response\n",
    "    SL = []\n",
    "    SL.append(0)\n",
    "    for t in range(1,tend-tbeg):\n",
    "        #print(t)\n",
    "        dSL = 0\n",
    "        for tp in range(0,t):\n",
    "            #print(t,tp)\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_RCP85=np.vstack([SL_wTd_nos_base_CISM_NCA_R1_RCP85, SL])\n",
    "\n",
    "    SL = []\n",
    "    SL.append(0)\n",
    "    for t in range(1,tend-tbeg):\n",
    "        #print(t)\n",
    "        dSL = 0\n",
    "        for tp in range(0,t):\n",
    "            #print(t,tp)\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_RCP85=np.vstack([SL_wTd_nos_base_CISM_NCA_R2_RCP85, SL])\n",
    "\n",
    "    SL = []\n",
    "    SL.append(0)\n",
    "    for t in range(1,tend-tbeg):\n",
    "        #print(t)\n",
    "        dSL = 0\n",
    "        for tp in range(0,t):\n",
    "            #print(t,tp)\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_RCP85=np.vstack([SL_wTd_nos_base_CISM_NCA_R3_RCP85, SL])\n",
    "\n",
    "    SL = []\n",
    "    SL.append(0)\n",
    "    for t in range(1,tend-tbeg):\n",
    "        #print(t)\n",
    "        dSL = 0\n",
    "        for tp in range(0,t):\n",
    "            #print(t,tp)\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_RCP85=np.vstack([SL_wTd_nos_base_CISM_NCA_R4_RCP85, SL])\n",
    "\n",
    "    SL = []\n",
    "    SL.append(0)\n",
    "    for t in range(1,tend-tbeg):\n",
    "        #print(t)\n",
    "        dSL = 0\n",
    "        for tp in range(0,t):\n",
    "            #print(t,tp)\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_RCP85=np.vstack([SL_wTd_nos_base_CISM_NCA_R5_RCP85, SL])\n",
    "\n",
    "SL_wTd_nos_base_CISM_NCA_SU_RCP85 = SL_wTd_nos_base_CISM_NCA_R1_RCP85+SL_wTd_nos_base_CISM_NCA_R2_RCP85+SL_wTd_nos_base_CISM_NCA_R3_RCP85+SL_wTd_nos_base_CISM_NCA_R4_RCP85+SL_wTd_nos_base_CISM_NCA_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": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "Time = range(1900,2100)\n",
    "ncfile = nc.Dataset('EnsembleSingleModelProjections/SL_wTd_nos_base_CISM_NCA_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_CISM_NCA_R1_RCP85\n",
    "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_CISM_NCA_R2_RCP85\n",
    "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_CISM_NCA_R3_RCP85\n",
    "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_CISM_NCA_R4_RCP85\n",
    "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_CISM_NCA_R5_RCP85\n",
    "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_CISM_NCA_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": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "SL_wTd_nos_base_CISM_NCA_SU_RCP85_50pc = np.percentile(SL_wTd_nos_base_CISM_NCA_SU_RCP85, 50, axis=0, out=None, overwrite_input=False, interpolation='linear', keepdims=False)\n",
    "SL_wTd_nos_base_CISM_NCA_SU_RCP85_83pc = np.percentile(SL_wTd_nos_base_CISM_NCA_SU_RCP85, 83.33, axis=0, out=None, overwrite_input=False, interpolation='linear', keepdims=False)\n",
    "SL_wTd_nos_base_CISM_NCA_SU_RCP85_17pc = np.percentile(SL_wTd_nos_base_CISM_NCA_SU_RCP85, 16.66, axis=0, out=None, overwrite_input=False, interpolation='linear', keepdims=False)\n",
    "SL_wTd_nos_base_CISM_NCA_SU_RCP85_95pc = np.percentile(SL_wTd_nos_base_CISM_NCA_SU_RCP85, 5, axis=0, out=None, overwrite_input=False, interpolation='linear', keepdims=False)\n",
    "SL_wTd_nos_base_CISM_NCA_SU_RCP85_05pc = np.percentile(SL_wTd_nos_base_CISM_NCA_SU_RCP85, 95, axis=0, out=None, overwrite_input=False, interpolation='linear', keepdims=False)\n",
    "SL_wTd_nos_base_CISM_NCA_SU_RCP85_99pc = np.percentile(SL_wTd_nos_base_CISM_NCA_SU_RCP85, 99, axis=0, out=None, overwrite_input=False, interpolation='linear', keepdims=False)\n",
    "SL_wTd_nos_base_CISM_NCA_SU_RCP85_01pc = np.percentile(SL_wTd_nos_base_CISM_NCA_SU_RCP85, 1, axis=0, out=None, overwrite_input=False, interpolation='linear', keepdims=False)\n",
    "\n",
    "np.savetxt(\"PercentilesSingleModelProjections/SL_wTd_nos_base_CISM_NCA_SU_RCP85_50pc.dat\", SL_wTd_nos_base_CISM_NCA_SU_RCP85_50pc, delimiter=\",\")\n",
    "np.savetxt(\"PercentilesSingleModelProjections/SL_wTd_nos_base_CISM_NCA_SU_RCP85_83pc.dat\", SL_wTd_nos_base_CISM_NCA_SU_RCP85_83pc, delimiter=\",\")\n",
    "np.savetxt(\"PercentilesSingleModelProjections/SL_wTd_nos_base_CISM_NCA_SU_RCP85_17pc.dat\", SL_wTd_nos_base_CISM_NCA_SU_RCP85_17pc, delimiter=\",\")\n",
    "np.savetxt(\"PercentilesSingleModelProjections/SL_wTd_nos_base_CISM_NCA_SU_RCP85_05pc.dat\", SL_wTd_nos_base_CISM_NCA_SU_RCP85_05pc, delimiter=\",\")\n",
    "np.savetxt(\"PercentilesSingleModelProjections/SL_wTd_nos_base_CISM_NCA_SU_RCP85_95pc.dat\", SL_wTd_nos_base_CISM_NCA_SU_RCP85_95pc, delimiter=\",\")\n",
    "np.savetxt(\"PercentilesSingleModelProjections/SL_wTd_nos_base_CISM_NCA_SU_RCP85_01pc.dat\", SL_wTd_nos_base_CISM_NCA_SU_RCP85_01pc, delimiter=\",\")\n",
    "np.savetxt(\"PercentilesSingleModelProjections/SL_wTd_nos_base_CISM_NCA_SU_RCP85_99pc.dat\", SL_wTd_nos_base_CISM_NCA_SU_RCP85_99pc, delimiter=\",\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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_RCP85[i,:])\n",
    "\n",
    "plt.show()\n",
    "fp3.savefig(\"Figures/SL_wTd_nos_base_CISM_NCA_SU_RCP85_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
}