greb_experiments.ipynb

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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Experiments with the GREB Model"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Initial experiments\n",
      "- Wind anomaly: \n",
      "    - v_anom.pos/neg\n",
      "    - u_anom.pos/neg\n",
      "    - u_vanom.pos/neg\n",
      "- Cloud anomaly: \n",
      "    - cld.inc\n",
      "    - cld.dec\n",
      "- Soil Moisture anomaly: \n",
      "    - smc.wet\n",
      "    - smc.dry\n",
      "- SST anomaly"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext autoreload\n",
      "%autoreload 2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The autoreload extension is already loaded. To reload it, use:\n",
        "  %reload_ext autoreload\n"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import iris\n",
      "import iris.quickplot as qplt\n",
      "import iris.plot as iplt\n",
      "import matplotlib.pyplot as plt\n",
      "import mycmaps as mc\n",
      "import greb_tools as gt\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 46
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Creat input files and run model"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Define the experiment name"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# exp = \"cld.pos\"\n",
      "# exp = \"smc.neg\"\n",
      "# exp = \"smc.pos\"\n",
      "# exp = \"clim\"\n",
      "# exp = \"u_anom.pos\"\n",
      "# exp = \"uv_anom.pos\"\n",
      "exp = \"v_anom.pos\"\n",
      "# exp = \"icealb\"\n",
      "print exp"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "v_anom.pos\n"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Create the input files."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import write_input as wi\n",
      "print exp\n",
      "if exp == \"v_anom.pos\":\n",
      "    wi.write_v_anom(\"pos\")\n",
      "if exp == \"uv_anom.pos\":\n",
      "    wi.write_v_anom(\"pos\")\n",
      "    wi.write_u_anom(\"pos\")   \n",
      "if exp == \"u_anom.pos\":\n",
      "    wi.write_u_anom(\"pos\")\n",
      "if exp == \"cld.pos\":\n",
      "    wi.write_cld(\"pos\")\n",
      "if exp == \"cld.neg\":\n",
      "    wi.write_cld(\"neg\")\n",
      "if exp == \"smc.pos\":\n",
      "    wi.write_smc(\"pos\")\n",
      "if exp == \"smc.neg\":\n",
      "    wi.write_smc(\"neg\")\n",
      "if exp == \"clim\":\n",
      "    print \"nothing needs doin'\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "smc.pos\n",
        "Soil moisture loaded\n",
        "Positive anomaly"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Writing soil.moisture.smc.anom/nc\n"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Copy the binary input files to the main directory"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import shutil as sh\n",
      "import glob\n",
      "import os\n",
      "import re\n",
      "\n",
      "main_dir = '/home/nicholat/project/greb/'\n",
      "input_dir = main_dir + 'input_files/'\n",
      "orig_input_dir = main_dir + 'orig_input/'\n",
      "\n",
      "# copy the original input files into the main folder\n",
      "for filename in glob.glob(os.path.join(orig_input_dir, '*')):\n",
      "    sh.copy(filename, main_dir)\n",
      "print \"Original input files copied to main dir\"\n",
      "print \"Anom placeholder bin files copied to main dir\"\n",
      "\n",
      "if exp == 'v_anom.pos':\n",
      "    sh.copyfile(input_dir+'meridional.wind.anom',\n",
      "                main_dir+'meridional.wind.anom')\n",
      "    print \"Anomalous meridional wind input copied to main dir\"\n",
      "    \n",
      "if exp == 'u_anom.pos':\n",
      "    sh.copyfile(input_dir+'zonal.wind.anom',\n",
      "                main_dir+'zonal.wind.anom')\n",
      "    print \"Anomalous zonal wind input copied to main dir\"\n",
      "    \n",
      "if exp == 'uv_anom.pos':\n",
      "    sh.copyfile(input_dir+'zonal.wind.anom',\n",
      "                main_dir+'zonal.wind.anom')\n",
      "    print \"Anomalous zonal wind input copied to main dir\"\n",
      "    sh.copyfile(input_dir+'meridional.wind.anom',\n",
      "                main_dir+'meridional.wind.anom')\n",
      "    print \"Anomalous meridional wind input copied to main dir\"\n",
      "\n",
      "if exp == 'cld.pos':\n",
      "    sh.copyfile(input_dir+'cloud.cover.cld.anom',\n",
      "                main_dir+'cloud.cover.anom')\n",
      "    with open('namelist','r') as f_in:\n",
      "        lines = f_in.readlines()\n",
      "        # do the replacement in the ith line.\n",
      "        lines[7] = re.sub('wnd','cld',lines[7])\n",
      "        print lines[7]\n",
      "    with open('namelist','w') as f_out:\n",
      "        f_out.writelines(lines)\n",
      "\n",
      "    print \"Anomalous cloud clover input copied to main dir\"\n",
      "    \n",
      "if exp == 'smc.pos':\n",
      "    sh.copyfile(input_dir+'soil.moisture.smc.anom',\n",
      "                main_dir+'soil.moisture.anom')\n",
      "    with open('namelist','r') as f_in:\n",
      "        lines = f_in.readlines()\n",
      "        # do the replacement in the ith line.\n",
      "        lines[7] = re.sub('wnd','smc',lines[7])\n",
      "        print lines[7]\n",
      "    with open('namelist','w') as f_out:\n",
      "        f_out.writelines(lines)\n",
      "\n",
      "    print \"Anomalous soil moisture input copied to main dir\"\n",
      "\n",
      "if exp == 'smc.neg':\n",
      "    sh.copyfile(input_dir+'soil.moisture.smc.anom',\n",
      "                main_dir+'soil.moisture.anom')\n",
      "    with open('namelist','r') as f_in:\n",
      "        lines = f_in.readlines()\n",
      "        # do the replacement in the ith line.\n",
      "        lines[7] = re.sub('wnd','smc',lines[7])\n",
      "        print lines[7]\n",
      "    with open('namelist','w') as f_out:\n",
      "        f_out.writelines(lines)\n",
      "\n",
      "    print \"Anomalous soil moisture input copied to main dir\"\n",
      "        "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Original input files copied to main dir\n",
        "Anom placeholder bin files copied to main dir\n",
        "anom = 'smc' ! character(len3) choose anomaly (smc (wind), smc, cld)\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Anomalous soil moisture input copied to main dir\n"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Check Input"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "anom_list = gt.read_anom_bin()\n",
      "fig = plt.figure(figsize=(5,5))\n",
      "plt.subplot(3,2,1)\n",
      "iplt.pcolormesh(anom_list[0][:,:,-1],cmap='bwr')\n",
      "plt.title(anom_list[0].long_name)\n",
      "plt.gca().coastlines()\n",
      "plt.subplot(3,2,2)\n",
      "iplt.pcolormesh(anom_list[1][:,:,-1],cmap='bwr')\n",
      "plt.title(anom_list[1].long_name)\n",
      "plt.gca().coastlines()\n",
      "plt.subplot(3,2,3)\n",
      "iplt.pcolormesh(anom_list[2][:,:,-1],cmap='bwr')\n",
      "plt.title(anom_list[2].long_name)\n",
      "plt.gca().coastlines()\n",
      "plt.subplot(3,2,4)\n",
      "iplt.pcolormesh(anom_list[3][:,:,-1],cmap='bwr')\n",
      "plt.title(anom_list[3].long_name)\n",
      "plt.gca().coastlines()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "<cartopy.mpl.feature_artist.FeatureArtist at 0x7f22f4dbef50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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kB/WjXno2ySbyQLq01mXC8AffeLm4xCu3pTrdNRP9y8V4ytq9We9JsX3bNNej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\nVaEKVXhgUWWgqlCFKjyw+H8HUwurGCUrJQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f22f932f3d0>"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Run the model"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print anom_list[2].data.max()\n",
      "qplt.pcmeshclf(anom_list[2][:,:,0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.0813679918647\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "<matplotlib.collections.QuadMesh at 0x7f22f4e29350>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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GjuTNrl1vKc/VtDRW7tnDkcOHiUpJ4bT8Pj4OCeGjkBCoV4/Zu3YR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\n3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       "text": [
        "<matplotlib.figure.Figure at 0x7f22f5ce2690>"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%bash\n",
      "cat namelist"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "&NUMERICS\n",
        "time_flux = 3  ! length of flux corrections run [yrs]\n",
        "time_ctrl = 5  ! length of control run [yrs\n",
        "time_scnr = 50  ! length of scenariorun [yrs\n",
        "/\n",
        "&PHYSICS\n",
        "log_exp = 10 ! complete GREB model; 2xCO2 forcing\n",
        "anom = 'smc' ! character(len3) choose anomaly (smc (wind), smc, cld)\n",
        "/\n"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Run GREB model"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%bash\n",
      "./compile_run_greb.sh"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % start climate shell\n",
        " % diagonstic point lat/lon:    30.0000000       172.500000    \n",
        " % time flux/control/scenario:            3           5          50\n",
        " % flux correction    340.000000    \n",
        "   0.00000000       4.77062988       26.7869568       4.84933472    \n",
        "   0.00000000       4.77062988       26.7869568       4.84933472    \n",
        "   0.00000000       4.77062988       26.7869568       4.84933472    \n",
        " % CONTROL RUN CO2=   340.000000       time=           5 yr\n",
        "   1970.00000       4.77069092       26.7869873       4.84936523    \n",
        "   1970.00000       4.77069092       26.7869873       4.84936523    \n",
        "   1970.00000       4.77069092       26.7869873       4.84936523    \n",
        "   1970.00000       4.77075195       26.7870178       4.84936523    \n",
        "   1970.00000       4.77069092       26.7870483       4.84939575    \n",
        " % SCENARIO EXP:           10   time=          50 yr\n",
        " anom = smc\n",
        "   1940.00000       4.77191162       26.7868652       4.82638550    \n",
        "   1941.00000       4.76702881       26.7859497       4.82315063    \n",
        "   1942.00000       4.76461792       26.7846985       4.82196045    \n",
        "   1943.00000       4.76266479       26.7836304       4.82101440    \n",
        "   1944.00000       4.76089478       26.7825928       4.82025146    \n",
        "   1945.00000       4.75939941       26.7816772       4.81930542    \n",
        "   1946.00000       4.75799561       26.7807312       4.81863403    \n",
        "   1947.00000       4.75671387       26.7797241       4.81787109    \n",
        "   1948.00000       4.75549316       26.7787476       4.81716919    \n",
        "   1949.00000       4.75439453       26.7780151       4.81652832    \n",
        "   1950.00000       4.75341797       26.7772217       4.81591797    \n",
        "   1951.00000       4.75241089       26.7764587       4.81536865    \n",
        "   1952.00000       4.75155640       26.7757263       4.81488037    \n",
        "   1953.00000       4.75079346       26.7749329       4.81436157    \n",
        "   1954.00000       4.75000000       26.7744446       4.81393433    \n",
        "   1955.00000       4.74932861       26.7738953       4.81362915    \n",
        "   1956.00000       4.74862671       26.7733765       4.81326294    \n",
        "   1957.00000       4.74798584       26.7729492       4.81301880    \n",
        "   1958.00000       4.74746704       26.7725220       4.81271362    \n",
        "   1959.00000       4.74688721       26.7720642       4.81240845    \n",
        "   1960.00000       4.74639893       26.7716980       4.81210327    \n",
        "   1961.00000       4.74594116       26.7713318       4.81185913    \n",
        "   1962.00000       4.74551392       26.7709961       4.81164551    \n",
        "   1963.00000       4.74514771       26.7707520       4.81146240    \n",
        "   1964.00000       4.74472046       26.7704468       4.81127930    \n",
        "   1965.00000       4.74435425       26.7702332       4.81106567    \n",
        "   1966.00000       4.74401855       26.7700500       4.81094360    \n",
        "   1967.00000       4.74371338       26.7698059       4.81076050    \n",
        "   1968.00000       4.74334717       26.7695618       4.81063843    \n",
        "   1969.00000       4.74313354       26.7692871       4.81057739    \n",
        "   1970.00000       4.74282837       26.7690735       4.81045532    \n",
        "   1971.00000       4.74255371       26.7689209       4.81039429    \n",
        "   1972.00000       4.74237061       26.7687988       4.81027222    \n",
        "   1973.00000       4.74218750       26.7686462       4.81015015    \n",
        "   1974.00000       4.74191284       26.7685852       4.80999756    \n",
        "   1975.00000       4.74176025       26.7683716       4.80993652    \n",
        "   1976.00000       4.74151611       26.7681580       4.80984497    \n",
        "   1977.00000       4.74139404       26.7680054       4.80975342    \n",
        "   1978.00000       4.74118042       26.7679138       4.80972290    \n",
        "   1979.00000       4.74102783       26.7677917       4.80963135    \n",
        "   1980.00000       4.74087524       26.7676697       4.80957031    \n",
        "   1981.00000       4.74075317       26.7675781       4.80953979    \n",
        "   1982.00000       4.74060059       26.7674866       4.80947876    \n",
        "   1983.00000       4.74047852       26.7674255       4.80941772    \n",
        "   1984.00000       4.74032593       26.7673035       4.80941772    \n",
        "   1985.00000       4.74023438       26.7672424       4.80938721    \n",
        "   1986.00000       4.74014282       26.7672119       4.80935669    \n",
        "   1987.00000       4.74005127       26.7671509       4.80932617    \n",
        "   1988.00000       4.73995972       26.7671509       4.80926514    \n",
        "   1989.00000       4.73983765       26.7671204       4.80917358    \n"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Copy output to bin_dir and rename"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Copy the output files to bin_files dir and rename\n",
      "bin_dir = main_dir + 'bin_files/'\n",
      "sh.copyfile(main_dir + 'control',bin_dir + 'control.' + exp + '.bin')\n",
      "sh.copyfile(main_dir + 'scenario',bin_dir + 'scenario.' + exp + '.bin')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ls bin_files/"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "control.all.pos.4ycomp.bin      scenario.all.pos.4ycomp.bin\r\n",
        "control.cld.pos.4ycomp.bin      scenario.cld.pos.4ycomp.bin\r\n",
        "control.cld.pos.bin             scenario.cld.pos.bin\r\n",
        "control.clim.bin                scenario.clim.bin\r\n",
        "control.icealb.bin              scenario.icealb.bin\r\n",
        "control.smc.dry.bin             scenario.smc.dry.bin\r\n",
        "control.smc.neg.bin             scenario.smc.neg.bin\r\n",
        "control.smc.pos.4ycomp.bin      scenario.smc.pos.4ycomp.bin\r\n",
        "control.smc.pos.bin             scenario.smc.pos.bin\r\n",
        "control.test.bin                scenario.test.bin\r\n",
        "control.u_anom.pos.4ycomp.bin   scenario.u_anom.pos.4ycomp.bin\r\n",
        "control.u_anom.pos.bin          scenario.u_anom.pos.bin\r\n",
        "control.uv_anom.pos.4ycomp.bin  scenario.uv_anom.pos.4ycomp.bin\r\n",
        "control.uv_anom.pos.bin         scenario.uv_anom.pos.bin\r\n",
        "control.v_anom.pos.4ycomp.bin   scenario.vanom.bin\r\n",
        "control.v_anom.pos.bin          scenario.v_anom.pos.4ycomp.bin\r\n",
        "control.vwind_1msanom.bin       scenario.v_anom.pos.bin\r\n",
        "scenario.4ycomp.cld.pos.bin     scenario.vwind_1msanom.bin\r\n"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Convert output binary files to netcdf"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import output_to_nc as b2n\n",
      "\n",
      "cube_list = b2n.write_exp(exp)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "./bin_files/scenario.smc.pos.bin\n",
        "tdim = 600\n",
        "Save cube as ./ncfiles/t_surf.smc.pos.nc"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "tdim = 600\n",
        "Save cube as ./ncfiles/t_atmos.smc.pos.nc"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "tdim = 600\n",
        "Save cube as ./ncfiles/t_ocean.smc.pos.nc"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "tdim = 600\n",
        "Save cube as ./ncfiles/vapour.smc.pos.nc"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "tdim = 600\n",
        "Save cube as ./ncfiles/albedo.smc.pos.nc"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Check Output"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# exp = \"cld.pos\"\n",
      "# exp = \"smc.neg\"\n",
      "exp = \"smc.pos\"\n",
      "# exp = \"clim\"\n",
      "# exp = \"u_anom.pos\"\n",
      "# exp = \"uv_anom.pos\"\n",
      "# exp = \"v_anom.pos\"\n",
      "# exp = \"icealb\"\n",
      "print exp"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "smc.pos\n"
       ]
      }
     ],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nc_dir = main_dir+'ncfiles/'\n",
      "t_surf_clim = iris.load_cube(nc_dir + '/t_surf.clim.nc')\n",
      "t_surf = iris.load_cube(nc_dir + '/t_surf.' + exp + '.nc')\n",
      "\n",
      "gt.try_cube(t_surf_clim)\n",
      "gt.try_cube(t_surf)\n",
      "\n",
      "# take the mean of last year \n",
      "t_surf_clim_mean = t_surf_clim[:,:,-13:-1].collapsed('time',iris.analysis.MEAN)\n",
      "t_surf_mean = t_surf[:,:,-13:-1].collapsed('time',iris.analysis.MEAN)\n",
      "dif = t_surf_mean - t_surf_clim_mean\n",
      "dif.long_name = 'Tsfc difference '+exp\n",
      "qplt.pcmeshclf(dif,vmin=-1.0,vmax=1.0)\n",
      "plt.savefig('./figures/tsfc_dif.'+exp+'.pdf')\n",
      "#fig = plt.figure(figsize=(12,8))\n",
      "#plt.subplot(2,2,1)\n",
      "#qplt.pcmeshclf(t_surf_clim_mean,hold=True,cmap=mc.jetwhite(),vmax = (273.15+30), vmin = (273.15-30))\n",
      "#plt.subplot(2,2,2)\n",
      "#qplt.pcmeshclf(t_surf_mean,hold=True,cmap=mc.jetwhite(),vmax = (273.15+30), vmin = (273.15-30))\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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hk2pSUwvU5l7gwn3RosWi1erk/PkLGQSfi4uL1KpVSw4fPiJJiYmyaeNG+Wj0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\nHwXPiTlkp1xsbDyTJv2Ivr4Bs2ZtVFlH2c96simksipbvo6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jTT59fX1WrFhP375dWLhw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       "text": [
        "<matplotlib.figure.Figure at 0x7f22e528b4d0>"
       ]
      }
     ],
     "prompt_number": 63
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%whos"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Variable           Type        Data/Info\n",
        "----------------------------------------\n",
        "anom_list          CubeList    0: cloud.cover.anom / (1)<...>longitude: 96; time: 730)\n",
        "b2n                module      <module 'output_to_nc' from 'output_to_nc.pyc'>\n",
        "bin_dir            str         /home/nicholat/project/greb/bin_files/\n",
        "cube_list          CubeList    0: surface_temperature / <...>longitude: 96; time: 600)\n",
        "dif                Cube        unknown / (K)            <...>         -              x\n",
        "exp                str         smc.neg\n",
        "f_in               file        <closed file 'namelist', <...>de 'r' at 0x7f56c174d300>\n",
        "f_out              file        <closed file 'namelist', <...>de 'w' at 0x7f56c174da50>\n",
        "fig                Figure      Figure(400x400)\n",
        "filename           str         /home/nicholat/project/gr<...>nput/meridional.wind.anom\n",
        "glob               module      <module 'glob' from '/usr<...>/lib/python2.7/glob.pyc'>\n",
        "gt                 module      <module 'greb_tools' from 'greb_tools.pyc'>\n",
        "input_dir          str         /home/nicholat/project/greb/input_files/\n",
        "iplt               module      <module 'iris.plot' from <...>-packages/iris/plot.pyc'>\n",
        "iris               module      <module 'iris' from '/usr<...>kages/iris/__init__.pyc'>\n",
        "lines              list        n=9\n",
        "main_dir           str         /home/nicholat/project/greb/\n",
        "mc                 module      <module 'mycmaps' from '/<...>lat/climate/mycmaps.pyc'>\n",
        "nc_dir             str         /home/nicholat/project/greb/ncfiles/\n",
        "np                 module      <module 'numpy' from '/us<...>ages/numpy/__init__.pyc'>\n",
        "orig_input_dir     str         /home/nicholat/project/greb/orig_input/\n",
        "os                 module      <module 'os' from '/usr/lib/python2.7/os.pyc'>\n",
        "plt                module      <module 'matplotlib.pyplo<...>s/matplotlib/pyplot.pyc'>\n",
        "qplt               module      <module 'iris.quickplot' <...>kages/iris/quickplot.py'>\n",
        "re                 module      <module 're' from '/usr/lib/python2.7/re.pyc'>\n",
        "sh                 module      <module 'shutil' from '/u<...>ib/python2.7/shutil.pyc'>\n",
        "t_surf             Cube        surface_temperature / (K)<...>rce: Data from GREB model\n",
        "t_surf_clim        Cube        surface_temperature / (K)<...>rce: Data from GREB model\n",
        "t_surf_clim_mean   Cube        surface_temperature / (K)<...>ds:\\n          mean: time\n",
        "t_surf_mean        Cube        surface_temperature / (K)<...>ds:\\n          mean: time\n",
        "wi                 module      <module 'write_input' from 'write_input.py'>\n"
       ]
      }
     ],
     "prompt_number": 99
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 99
    }
   ],
   "metadata": {}
  }
 ]
}