{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 5,
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 7,
   "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": 8,
   "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": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# BISI_LBL = BISI_LBL\n",
    "# Read response functions\n",
    "\n",
    "fname = \"../RFunctions/RF_BISI_LBL_BM08_R1.dat\" # File to read\n",
    "with open(fname) as f:\n",
    "    RF_BISI_LBL_BM08_R1 = np.array([float(row) for row in f])\n",
    "\n",
    "fname = \"../RFunctions/RF_BISI_LBL_BM08_R2.dat\" # File to read\n",
    "with open(fname) as f:\n",
    "    RF_BISI_LBL_BM08_R2 = np.array([float(row) for row in f])\n",
    "\n",
    "fname = \"../RFunctions/RF_BISI_LBL_BM08_R3.dat\" # File to read\n",
    "with open(fname) as f:\n",
    "    RF_BISI_LBL_BM08_R3 = np.array([float(row) for row in f])\n",
    "\n",
    "fname = \"../RFunctions/RF_BISI_LBL_BM08_R4.dat\" # File to read\n",
    "with open(fname) as f:\n",
    "    RF_BISI_LBL_BM08_R4 = np.array([float(row) for row in f])\n",
    "\n",
    "fname = \"../RFunctions/RF_BISI_LBL_BM08_R5.dat\" # File to read\n",
    "with open(fname) as f:\n",
    "    RF_BISI_LBL_BM08_R5 = np.array([float(row) for row in f])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5234 0.5555555555555556\n",
      "0.89565 0.6666666666666666\n",
      "0.52355 0.5555555555555556\n",
      "0.46895 1.0\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_BISI_LBL_R1_RCP85 = [0] * (tend-tbeg)\n",
    "SL_wTd_nos_base_BISI_LBL_R2_RCP85 = [0] * (tend-tbeg)\n",
    "SL_wTd_nos_base_BISI_LBL_R3_RCP85 = [0] * (tend-tbeg)\n",
    "SL_wTd_nos_base_BISI_LBL_R4_RCP85 = [0] * (tend-tbeg)\n",
    "SL_wTd_nos_base_BISI_LBL_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",
    "        #print(t)\n",
    "        dSL = 0\n",
    "        for tp in range(0,t):\n",
    "            #print(t,tp)\n",
    "            dSL = dSL + M_R1[tp]*RF_BISI_LBL_BM08_R1[t-tp]\n",
    "        SL.append(dSL)\n",
    "    SL_wTd_nos_base_BISI_LBL_R1_RCP85=np.vstack([SL_wTd_nos_base_BISI_LBL_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_BISI_LBL_BM08_R2[t-tp]\n",
    "        SL.append(dSL)\n",
    "    SL_wTd_nos_base_BISI_LBL_R2_RCP85=np.vstack([SL_wTd_nos_base_BISI_LBL_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_BISI_LBL_BM08_R3[t-tp]\n",
    "        SL.append(dSL)\n",
    "    SL_wTd_nos_base_BISI_LBL_R3_RCP85=np.vstack([SL_wTd_nos_base_BISI_LBL_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_BISI_LBL_BM08_R4[t-tp]\n",
    "        SL.append(dSL)\n",
    "    SL_wTd_nos_base_BISI_LBL_R4_RCP85=np.vstack([SL_wTd_nos_base_BISI_LBL_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_BISI_LBL_BM08_R5[t-tp]\n",
    "        SL.append(dSL)\n",
    "    SL_wTd_nos_base_BISI_LBL_R5_RCP85=np.vstack([SL_wTd_nos_base_BISI_LBL_R5_RCP85, SL])\n",
    "\n",
    "SL_wTd_nos_base_BISI_LBL_SU_RCP85 = SL_wTd_nos_base_BISI_LBL_R1_RCP85+SL_wTd_nos_base_BISI_LBL_R2_RCP85+SL_wTd_nos_base_BISI_LBL_R3_RCP85+SL_wTd_nos_base_BISI_LBL_R4_RCP85+SL_wTd_nos_base_BISI_LBL_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": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200\n",
      "20001\n"
     ]
    }
   ],
   "source": [
    "Time = range(1900,2100)\n",
    "print(len(Time))\n",
    "print(len(SL_wTd_nos_base_BISI_LBL_SU_RCP85[:,0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "ncfile = nc.Dataset('EnsembleSingleModelProjections/SL_wTd_nos_base_BISI_LBL_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_BISI_LBL_R1_RCP85\n",
    "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_BISI_LBL_R2_RCP85\n",
    "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_BISI_LBL_R3_RCP85\n",
    "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_BISI_LBL_R4_RCP85\n",
    "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_BISI_LBL_R5_RCP85\n",
    "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_BISI_LBL_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": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "SL_wTd_nos_base_BISI_LBL_SU_RCP85_50pc = np.percentile(SL_wTd_nos_base_BISI_LBL_SU_RCP85, 50, axis=0, out=None, overwrite_input=False, interpolation='linear', keepdims=False)\n",
    "SL_wTd_nos_base_BISI_LBL_SU_RCP85_83pc = np.percentile(SL_wTd_nos_base_BISI_LBL_SU_RCP85, 83.33, axis=0, out=None, overwrite_input=False, interpolation='linear', keepdims=False)\n",
    "SL_wTd_nos_base_BISI_LBL_SU_RCP85_17pc = np.percentile(SL_wTd_nos_base_BISI_LBL_SU_RCP85, 16.66, axis=0, out=None, overwrite_input=False, interpolation='linear', keepdims=False)\n",
    "SL_wTd_nos_base_BISI_LBL_SU_RCP85_95pc = np.percentile(SL_wTd_nos_base_BISI_LBL_SU_RCP85, 5, axis=0, out=None, overwrite_input=False, interpolation='linear', keepdims=False)\n",
    "SL_wTd_nos_base_BISI_LBL_SU_RCP85_05pc = np.percentile(SL_wTd_nos_base_BISI_LBL_SU_RCP85, 95, axis=0, out=None, overwrite_input=False, interpolation='linear', keepdims=False)\n",
    "SL_wTd_nos_base_BISI_LBL_SU_RCP85_99pc = np.percentile(SL_wTd_nos_base_BISI_LBL_SU_RCP85, 99, axis=0, out=None, overwrite_input=False, interpolation='linear', keepdims=False)\n",
    "SL_wTd_nos_base_BISI_LBL_SU_RCP85_01pc = np.percentile(SL_wTd_nos_base_BISI_LBL_SU_RCP85, 1, axis=0, out=None, overwrite_input=False, interpolation='linear', keepdims=False)\n",
    "\n",
    "np.savetxt(\"PercentilesSingleModelProjections/SL_wTd_nos_base_BISI_LBL_SU_RCP85_50pc.dat\", SL_wTd_nos_base_BISI_LBL_SU_RCP85_50pc, delimiter=\",\")\n",
    "np.savetxt(\"PercentilesSingleModelProjections/SL_wTd_nos_base_BISI_LBL_SU_RCP85_83pc.dat\", SL_wTd_nos_base_BISI_LBL_SU_RCP85_83pc, delimiter=\",\")\n",
    "np.savetxt(\"PercentilesSingleModelProjections/SL_wTd_nos_base_BISI_LBL_SU_RCP85_17pc.dat\", SL_wTd_nos_base_BISI_LBL_SU_RCP85_17pc, delimiter=\",\")\n",
    "np.savetxt(\"PercentilesSingleModelProjections/SL_wTd_nos_base_BISI_LBL_SU_RCP85_05pc.dat\", SL_wTd_nos_base_BISI_LBL_SU_RCP85_05pc, delimiter=\",\")\n",
    "np.savetxt(\"PercentilesSingleModelProjections/SL_wTd_nos_base_BISI_LBL_SU_RCP85_95pc.dat\", SL_wTd_nos_base_BISI_LBL_SU_RCP85_95pc, delimiter=\",\")\n",
    "np.savetxt(\"PercentilesSingleModelProjections/SL_wTd_nos_base_BISI_LBL_SU_RCP85_01pc.dat\", SL_wTd_nos_base_BISI_LBL_SU_RCP85_01pc, delimiter=\",\")\n",
    "np.savetxt(\"PercentilesSingleModelProjections/SL_wTd_nos_base_BISI_LBL_SU_RCP85_99pc.dat\", SL_wTd_nos_base_BISI_LBL_SU_RCP85_99pc, delimiter=\",\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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_BISI_LBL_SU_RCP85[i,:])\n",
    "\n",
    "plt.show()\n",
    "fp3.savefig(\"Figures/SL_wTd_nos_base_BISI_LBL_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
}