CheckGMREDIruns.ipynb

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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "What happened in the GMREDI runs?\n",
      "===================================================\n",
      "\n",
      "360x360x90_8Tr_LinProfiles_BarkleyHyd_GMREDI\n",
      "--------------------------------------------------------------"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      " Run01 - Parameters"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#KRM\n",
      "\n",
      "import numpy as np\n",
      "\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "import matplotlib.colors as mcolors\n",
      "\n",
      "from math import *\n",
      "\n",
      "import scipy.io\n",
      "\n",
      "import scipy as spy\n",
      "\n",
      "%matplotlib inline\n",
      "\n",
      "from netCDF4 import Dataset\n",
      "\n",
      "import pylab as pl\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#'''\n",
      "#NAME\n",
      "#    Custom Colormaps for Matplotlib\n",
      "#PURPOSE\n",
      "#    This program shows how to implement make_cmap which is a function that\n",
      "#    generates a colorbar.  If you want to look at different color schemes,\n",
      "#    check out https://kuler.adobe.com/create.\n",
      "#PROGRAMMER(S)\n",
      "#    Chris Slocum\n",
      "#REVISION HISTORY\n",
      "#    20130411 -- Initial version created\n",
      "#    20140313 -- Small changes made and code posted online\n",
      "#    20140320 -- Added the ability to set the position of each color\n",
      "#'''\n",
      "\n",
      "def make_cmap(colors, position=None, bit=False):\n",
      "    #'''\n",
      "    #make_cmap takes a list of tuples which contain RGB values. The RGB\n",
      "    #values may either be in 8-bit [0 to 255] (in which bit must be set to\n",
      "    #rue when called) or arithmetic [0 to 1] (default). make_cmap returns\n",
      "    #a cmap with equally spaced colors.\n",
      "    #Arrange your tuples so that the first color is the lowest value for the\n",
      "    #colorbar and the last is the highest.\n",
      "    #position contains values from 0 to 1 to dictate the location of each color.\n",
      "    #'''\n",
      "    import matplotlib as mpl\n",
      "    import numpy as np\n",
      "    bit_rgb = np.linspace(0,1,256)\n",
      "    if position == None:\n",
      "        position = np.linspace(0,1,len(colors))\n",
      "    else:\n",
      "        if len(position) != len(colors):\n",
      "            sys.exit(\"position length must be the same as colors\")\n",
      "        elif position[0] != 0 or position[-1] != 1:\n",
      "            sys.exit(\"position must start with 0 and end with 1\")\n",
      "    if bit:\n",
      "        for i in range(len(colors)):\n",
      "            colors[i] = (bit_rgb[colors[i][0]],\n",
      "                         bit_rgb[colors[i][1]],\n",
      "                         bit_rgb[colors[i][2]])\n",
      "    cdict = {'red':[], 'green':[], 'blue':[]}\n",
      "    for pos, color in zip(position, colors):\n",
      "        cdict['red'].append((pos, color[0], color[0]))\n",
      "        cdict['green'].append((pos, color[1], color[1]))\n",
      "        cdict['blue'].append((pos, color[2], color[2]))\n",
      "\n",
      "    cmap = mpl.colors.LinearSegmentedColormap('my_colormap',cdict,256)\n",
      "    return cmap"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def unstagger(ugrid, vgrid):\n",
      "    \"\"\"Interpolate u and v component values to values at grid cell centres.\n",
      "\n",
      "    The shapes of the returned arrays are 1 less than those of\n",
      "    the input arrays in the y and x dimensions.\n",
      "\n",
      "    :arg ugrid: u velocity component values with axes (..., y, x)\n",
      "    :type ugrid: :py:class:`numpy.ndarray`\n",
      "\n",
      "    :arg vgrid: v velocity component values with axes (..., y, x)\n",
      "    :type vgrid: :py:class:`numpy.ndarray`\n",
      "\n",
      "    :returns u, v: u and v component values at grid cell centres\n",
      "    :rtype: 2-tuple of :py:class:`numpy.ndarray`\n",
      "    \"\"\"\n",
      "    u = np.add(ugrid[..., :-1], ugrid[..., 1:]) / 2\n",
      "    v = np.add(vgrid[..., :-1, :], vgrid[..., 1:, :]) / 2\n",
      "    return u[..., 1:, :], v[..., 1:]\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Get field from MITgcm netCDF output\n",
      "#\n",
      "''' :statefile : string with /path/to/state.0000000000.t001.nc\n",
      "    :fieldname : string with the variable name as written on the netCDF file ('Temp', 'S','Eta', etc.)'''\n",
      "\n",
      "def getField(statefile, fieldname):\n",
      "    \n",
      "    StateOut = Dataset(statefile)\n",
      "    \n",
      "    Fld = StateOut.variables[fieldname][:]\n",
      "    \n",
      "    shFld = np.shape(Fld)\n",
      "    \n",
      "        \n",
      "    if len(shFld) == 2:\n",
      "        \n",
      "        Fld2 = np.reshape(Fld,(shFld[0],shFld[1])) # reshape to pcolor order\n",
      "        return Fld2 \n",
      "    \n",
      "    elif len(shFld) == 3:\n",
      "        \n",
      "        Fld2 = np.zeros((shFld[0],shFld[1],shFld[2])) \n",
      "        Fld2 = np.reshape(Fld,(shFld[0],shFld[1],shFld[2])) # reshape to pcolor order\n",
      "        return Fld2\n",
      "        \n",
      "    elif len(shFld) == 4:\n",
      "        \n",
      "        Fld2 = np.zeros((shFld[0],shFld[1],shFld[2],shFld[3])) \n",
      "        Fld2 = np.reshape(Fld,(shFld[0],shFld[1],shFld[2],shFld[3])) # reshape to pcolor order\n",
      "        return Fld2\n",
      "        \n",
      "    else:\n",
      "        \n",
      "        print (' Check size of field ')\n",
      "    \n",
      "    \n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Inquire variable from NetCDF -  RUN01"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "filename='/ocean/kramosmu/MITgcm/CanyonUpwelling/360x360x90_8Tr_LinProfiles_BarkleyHyd_GMREDI/run01/state.0000000000.glob.nc'\n",
      "StateOut = Dataset(filename)\n",
      "\n",
      "#for dimobj in StateOut.variables.values():\n",
      "#   print dimobj\n",
      "\n",
      "\n",
      "filename2='/ocean/kramosmu/MITgcm/CanyonUpwelling/360x360x90_8Tr_LinProfiles_BarkleyHyd_GMREDI/run01/grid.glob.nc'\n",
      "GridOut = Dataset(filename2)\n",
      "\n",
      "#for dimobj in GridOut.variables.values():\n",
      "#    print dimobj\n",
      "\n",
      "\n",
      "filename3='/ocean/kramosmu/MITgcm/CanyonUpwelling/360x360x90_8Tr_LinProfiles_BarkleyHyd_GMREDI/run01/ptracers.0000000000.glob.nc'\n",
      "PtracersOut = Dataset(filename3)\n",
      "\n",
      "#for dimobj in PtracersOut.variables.values():\n",
      "#    print dimobj\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# General input\n",
      "\n",
      "nx = 360\n",
      "ny = 360\n",
      "nz = 90\n",
      "\n",
      "nt = 21 # t dimension size \n",
      "\n",
      "z = StateOut.variables['Z']\n",
      "print(z[10])\n",
      "Time = StateOut.variables['T']\n",
      "#print(Time[:])\n",
      "\n",
      "xc = getField(filename2, 'XC') # x coords tracer cells\n",
      "\n",
      "yc = getField(filename2, 'YC') # y coords tracer cells\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "-52.5\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Geometric parameters:\n",
      "\n",
      "* Canyon length 16180.0 m\n",
      "* Canyon width at shelf break 13005.0 m\n",
      "* Canyon width at half-length 7660.0 m\n",
      "* Radius of curvature upstream side 5370.0 m\n",
      "* Shelf break depth 150 m"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bathy = getField(filename2, 'Depth')\n",
      "\n",
      "plt.rcParams.update({'font.size': 15})\n",
      "\n",
      "fig = plt.figure(figsize=(12,9))\n",
      "\n",
      "plt.plot(xc[::4,::4],yc[::4,::4],linewidth=0.75, linestyle='-', color='0.75')\n",
      "\n",
      "lev = range(200,1200,100)\n",
      "\n",
      "levels = [50,100,150]+lev\n",
      "\n",
      "CS = plt.contour(xc,yc,bathy,levels,colors='k',linewidths=[1,1,2.25,1,1,1,1,1,1,1,1,1,1] )\n",
      "\n",
      "plt.clabel(CS, \n",
      "           inline=1,\n",
      "          fmt='%1.1f',\n",
      "           fontsize=14)\n",
      "\n",
      "\n",
      "\n",
      "plt.xticks(np.arange(0,120000,10000), [' ','10','20','30','40','50','60','70','80','90','100','110','120'])\n",
      "plt.yticks(np.arange(0,90000,10000), [' ','10','20','30','40','50','60','70','80','90'])\n",
      "\n",
      "plt.xlabel('km',fontsize=14)\n",
      "plt.ylabel('km',fontsize=14)\n",
      "\n",
      "plt.title('Depth (m)',fontsize=16)\n",
      "\n",
      "\n",
      "#plt.savefig('AcrossShoreLines.png', bbox_inches='tight')\n",
      "#plt.savefig('AcrossShoreLines.pdf', bbox_inches='tight')\n",
      "\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "<matplotlib.text.Text at 0x49621d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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NaRIRERERaUxHmwf7+eef8eLFC9VzQRAwY8YMXLx4ES4uLgCAkJAQBAcHY+HChbC3t8ei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6dHXFwc8PaBKysri7a2diEZfX39Uit7xcfHF9Grr69f4i54oNArxup43VjVOqtK34fqqWz7irYr\nS760EKD3YzQLHroFOiUSCdnZ2dKf8/PzycjIQCKRSI+8vDxSUlKk93Nzc3n+/Dn5+fnk5+eTl5dH\nVlYWMTEx5OXlkZuby5s3b7hy5Qo5OTnk5OSQnZ2NRCJh//79ZGVlkZGRgZ6eHufOnQPeZtiIiIig\nZ8+eTJ48mYyMDJKTkxkzZgxt27ZFUVERPT09DA0NGT16NPHx8Whra+Pi4oKpqSlJSUmFDFOxWFwt\nxvO7ZGZmMnPmTOrVq8fevXvLnSYtJCSEmJgYjh07hpGRUYlyqampBAYGlloe+82bN1y/fp0DBw6U\n2ueRI0c+22xHNWvW5OjRo0VqCLyLrKwsI0aMwMfHh1WrVhUro6amhouLC9OnT2fDhg0l9rdq1Sqc\nnZ0ZM2YMmzdvLnN81tbWSCQSTp06Rdu2bfH39y/fxCqJtrY2ffr0kYbniMVi0tLSiI+PJyoqirS0\nNHJycggNDeXp06c8e/aMUaNGYWtri4aGBjo6OkyZMoVJkyYhLy+PvLw8TZs2pV27dpw7dw4ZGRmU\nlJQwMTHh77//RlFREQUFBRQVFZFIJISHh0vbZWRkcP/+fWRlZZGVlUVGRobs7GyeP3+OSCRCRkaG\n3NxckpOTpecikUi6jhT8Pt9dZ969lpubW+h3XrBevXutuPXtXR1lUdXrbVW3q2od1aHrS9P5Jer9\najzUDx8+RCQSVejw9vYuUV9+fj4tW7bE09MTKysr3NzcGDduXLFVvd7nQ+M4K0Pbtm2RkZEhJCQE\nGRmZKj2qWmdV6ftQPZVtX9F2ZcmHhIQgJydX7BESEoKCgoL0CAkJQVFRESUlJZSVlVFRUeHs2bOo\nqamhpqaGuro6586dQ0tLC21tbXR1ddHX1yc0NBQTExNq1apFnTp1uHjxIo0aNaJJkyY0b96cNm3a\nIBaL6dy5Mz169MDJyYk7d+4wYsQIfvrpJ6ZMmcK8efN48uQJvr6+/P3335w6dYrU1FTu3r1LVFQU\ncXFx9OzZE2NjY3755RcWLVrEjh07aNGiBcnJyaSkpEizJFhaWvLbb78xffp0XF1dpXP5mJw9exYr\nKysePHiAtbV1uY3pGzdu4OLigqWlJQ0aNChRLisri7t37/Lbb7+Vmubu8ePHeHp6oqioWGq/R44c\noWfPnuUa48em4Hf3/qbs9xk3bhx+fn6leqDnzZvH4cOHOXXqVIkyMjIybN26lWvXrpW6jr+LnJwc\nu3fvZsyYMbRv356HDx9KDcSPgaysLMbGxtjb22NiYsK8efPw9fXl+PHjXL9+nXbt2pGVlUVUVBQH\nDx6kfv36zJgxgwkTJjBy5EipcRwVFcWtW7c4f/48SUlJ+Pn58fvvv7NkyRJmzJjB/fv3GTVqFEOH\nDmXAgAHcuHGDrl270rFjR2xsbLC2tiY8PJxGjRphYWFBvXr1CAsLo1atWhgbG6Ovr4+uri7nz59H\nS0uLGjVqoKmpKV1nVFVVUVFRQVlZmbNnz0qN+YL1SUFBAXl5+UJr2PvrWoFhX561tKrX2+p8LlXl\ns/JLeJZXl87PWW9pfDUealNT0yr91mFkZFTEO9awYUMePXoEIE1tlZCQUGgzUkJCgvSegYEBeXl5\nJCUlFfJSx8fHFypc8z4GBgZFvNHv6i2OgphssVhc5d++qlpnVen7UD2VbV/RdmXJi8XiEj3UYrG4\nkIf6/fOCa+8aBmKxuMhrbbFYXCgTg1gsLhJTKhaLC72OF4vFRYqgiMVihg8fXuh80KBBRWTeLbIk\nFotRUlIqdn6fgvz8fCZNmoS/vz9r167Fycmp3MVeoqOj6dGjB6tXry4zF7S3tzeNGzfG1dW1RJmH\nDx+SlJTEmDFjStWVk5PDrVu36NixY7nG+Sno1asXBw8epHHjxiXKGBsb4+TkxO+//15imrwaNWqw\nceNGRowYwfXr10vUpaamxtGjR7Gzs8Pa2rpchb1EIhGjRo2iZ8+enD59mrZt27Jjx45SC4d9TGRl\nZdHR0UFHR4f09PRCcyooPlOQShbe/m39888/hXSIxWIiIiIKncfExBSReffv//31oeDau+vI++tM\nwbXyrE+lrW9lraVVvd5Wdbuq1lEdur40nZ+z3tIcpl+Nh7qqsbW1LVLNLCoqCjMzMwDq1KmDgYEB\nJ06ckN7PzMzk3LlztGvXDoCWLVsiLy9fSObJkydERkaWmrnExsaGoKCgQteCgoKwtbX90GkJCPxn\nyc/Px8/Pj0uXLvH48WOuX7+Ok5NTuds/ePCALl26MHv27CJfIt4nPDwcX19fzM3NS12Aly9fjqGh\nYZlFZ169eoW9vX2ZXuxPSc+ePYusW8UxefJk1q5dWygb0vv06NEDe3t7pk6dWqouPT09Tp48ydOn\nT0sNEXmfWrVq0aRJE0aPHk379u0ZO3YsOTk55W4vICAg8D6CQV0CEyZMICwsjM2bNxMdHc2ePXtY\ns2aNNK2VSCRi/PjxLFmyhH/++YebN2/i5uaGuro63333HQCampqMGDGCyZMn8++//yIWixk6dChW\nVlaF8tV27tyZ6dOnS8+9vLw4deoUS5YsITIykkWLFhEcHMz48eM/7ocgIPCVcP78eWxsbFizZg2W\nlpYEBAQU2dtQGpmZmdjb2/Pzzz8zcuTIUmXT09MZPnw4v/32W6k5pZ89e8aOHTvKLBoDb9N4fu5l\nsG1sbIiIiCgz77OFhQX29vasW7euVLnffvuNAwcO8OrVq1LlateujZWVFQsWLODPP/+s0Jjd3d25\ne/cuCgoKXL58md9++63MjY4CAgICxSEY1CVgbW3N/v37CQoKokmTJvz666/Mnz+/0KvZyZMnM2HC\nBDw8PGjVqhUJCQmcOHGiUOEGHx8fnJycGDRoEHZ2dmhoaHDw4MFCXquYmJhCIR42Njb4+/uzZcsW\nrKys2L59OwEBAbRq1erjTF5A4Cvh5s2b3Lp1C1dXVzw9PQkLC0NDQ6NCOu7du8e1a9eYNGkSnp6e\nZcpPmDCB5s2bM2TIkFLllixZwvDhw0s1uuHtpsXnz5+XWWDmU1OzZk0cHBzYvXt3mbK//vorK1as\nKHUzrpaWFtu2bSMyMpLExMRS9SkpKXHq1CkWLFhQanrC4tDW1sbHx4dmzZoRHBxMw4YN2bZtm1Cw\nRUBAoEJ8NTHU1UHPnj0xNDQsNTZv9uzZzJ49u8T7CgoKrF69ukj+1XcfEMWlkRowYAADBgyoxKgF\nBARu3LjBggULOH36NJs2bSIqKqpScdzXr1+nR48ebNy4sVwbAvft28fJkycRi8Wlhno8ffoUPz8/\nbt68WWr2HoDjx4+jrq5e6h6KzwVXV1d8fX358ccfS5X75ptv6NixI0+fPqVly5YlyhWUNR8+fDiH\nDx8udVNQ/fr1CQkJoUuXLtJqiRXZIK6iosLBgwc5c+YMM2bMYOHChaxatYrc3Nwqy3UuICDw9SJ4\nqAUEBL4KCnJd9+nTB0dHR1q2bMn9+/cxMTGplDEdGhpK165dWbFiBYaGhmXKZ2ZmMmbMGPz9/cv0\ngs+fP58RI0aUS+/x48epWbNmucf9KenSpQvnzp0jIyOjTFlvb28eP37M69evS5UzMzMjOTmZ5cuX\nl6nTzMyMM2fOkJiYyM8//1ypzUcdOnTgzJkzrF27lmfPnmFubs6aNWtKjfkWEBAQEAxqAQGBL5o3\nb96wefNmWrRoQVRUFL179yY2NpZffvkFNTW1Sul8/fo1/fr1Y8uWLWVuQIS3xvStW7eYMWNGmaFZ\n9+7dY8+ePUyZMqVMvRkZGezduxddXd1yj/1ToqWlhZ2dHXv27ClTtlGjRmhra7Ns2bJS5UQiEf7+\n/qxYsaLE0uXvYmRkRLNmzThz5gweHh6V2mwoEono0qULVlZW7Nq1i+DgYMzMzLh//36VlDEXEBD4\n+hAMagEBgS8OiURCaGgod+7cwdjYmAMHDrBo0SJatWrFqFGjKp2mTyKRSMuJ7969u1yFVCQSCR4e\nHigrKzNu3Lgy5adPn87EiRPLtSny0KFDtGzZ8rPO7vE+P/zwA35+fuWSNTMz448//uDp06elytWu\nXZutW7fi6upartzRcnJyBAUF8fDhQzp27MizZ8/KNZ7iaNu2Lfv27SM8PByRSIStrS2tW7dm48aN\nQjlxAQEBKYJBLSAg8MUQGxvLihUrsLS0ZNiwYWhoaBAVFUVgYCDdu3f/IN1v3rxh8ODBHDhwgJYt\nW2Jvb1+uduvXrycsLAwLC4syY3ZTUlIICwsrd8aeI0eO8O2335ZL9nOhZ8+ehIaGlrrhsABFRUXc\n3d2ZOXNmmbLdunVj7Nix3Lp1q1zlw2vUqMHBgwfp3r07bdq0KTP7SFmYmZlRt25dnj59yty5cwkM\nDMTY2JioqCjOnj0rpN0TEPiPIxjUAgICny35+flER0ezaNEiLl++jI2NDdevX2fjxo1ERUVhbGyM\nnp7eB/eTkZGBjY0NysrK0mpv5eHUqVPMmTOHwMBAZGVlS5WVSCTExMQwf/78clWFTEtL49ChQ59t\nufGSUFVVxc7OjufPn5dLftq0aRw+fLjMKovw1rtfYISXJz5aRkaGWbNmsWrVKm7cuMHGjRs/uFiE\nvLw83bt35+DBg9y+fRslJSXGjx+Pvr4+bm5uJCcnC55rAYH/IIJBLSAg8Fnx6NEjNm3axJ07d9DT\n08PBwYHo6GgsLCyIi4tjy5Yt2NnZVSiDQ0lIJBLWr1+PWCxm7Nix/PXXX+UOF0lPT8fV1ZXdu3dT\nr169MuX37t1LTk5Omen0CtixYwft27enTp065ZL/nBg/fnyZYRwFaGhoMGXKlHJ57UUiEQ0bNuTO\nnTssWLCg3ONxcnKiefPmrFq1ioEDB5aZhq+8GBoaUrt2bSIiIrh58yZNmjQhOjoaXV1d7OzsmDNn\nDhcuXKiWim8CAgKfF4JBLSAg8Ml49uwZx44d48mTJ7i4uGBiYoK1tTWnTp1CS0uLq1ev8ujRIzZv\n3oyGhkapadMqSlpaGt999x2bN2+mWbNmjB49utxG+sOHD7lx4wZLliyhU6dOZcq/fv0aLy8vLCws\nyp2C7a+//mLEiBHlkv3c6Nq1K9nZ2aWWDn8XLy8vkpKSSEhIKFNWRkaGwMBANm/eXKFCLsrKyoSH\nh1O/fn2sra0/OATkfYyMjJg0aRItW7bkxYsXzJo1i7S0NMaMGcP58+fp0KEDU6dOZc+ePaSlpZUr\nJEZA4EvHzMwMGRmZIkfv3r2Bt04Nb29vjI2NUVFRwd7evsjbqqysLH766Sd0dXVRU1OjX79+5frC\nvm/fPiwtLVFSUqJx48bs37+/WuZYgGBQCwgIVDsSiYSoqCgOHTokrQDauHFjGjduzIoVK8jIyKBP\nnz6EhISQkJDAzp07MTAwKFcVwcoQFhZG8+bNUVZW5uzZs+UKwSggJSWF7t27Y2JigpubW7nazJw5\nk969e5e7qMyNGzd48uQJ3bp1K/e4PidkZWUxMDBg06ZN5ZKXk5Nj/fr1xMTElFkZEd56hoOCgpgz\nZw6HDh0q97iUlJRYsmQJPj4+0lzlubm55W5fXpSVlXF0dGTZsmVcu3YNGxsbZs6ciZKSErt27eLO\nnTvo6+vTo0cPpkyZgp+fH6mpqbx586bKxyIg8CmJiIggPj5eely5cgWRSCTNnrR06VJWrFjB2rVr\nuXz5Mnp6enTt2rVQ2NT48eP5+++/8ff35+zZs6SkpNC7d+9Sv5SGhoYyePBghg4dyrVr1xgyZAjO\nzs7cvHmz2uYqZKsXEBD4ICQSCVlZWSQlJREXF8fr16/x8fEhNjaW2NhY+vbti6OjIyoqKjRq1AhL\nS0uaNm2Kn58fTZs2RU5ODrFYXGoBpaoiPT2dmJgYPDw8WLduHf37969Q+6ysLFxdXenYsSPGxsbl\nanPp0iX27dvHrVu3ePjwYbnabNu2jaFDh37RBUX09fXx9/dn+fLl5YpJb926NQ8ePGDq1Kls2LCh\nTPn69euzd+9e+vXrx759+yo0tv79+xMWFsbMmTM5ePAgGzdurFD7iiIrK4ujoyHQWaEAACAASURB\nVCOOjo4AiMVixGIxERER3Lhxg8OHD9OlSxd69uyJqakpenp6jBo1ikOHDmFqakrt2rUxNjYmLy+P\nvLy8MuP1BQQ+F97PZrRx40Y0NTVxcXFBIpHg4+PDtGnTcHJyAmDr1q3o6elx9OhRbG1tSU5OxtfX\nly1bttC5c2cA/Pz8MDU15eTJk9K/qffx8fHBwcGBadOmAW/3X5w+fZqdO3cydOjQapnrl7taCwgI\nVBkSiQSJRMKrV6948+YNaWlppKamcurUKVJSUkhOTubVq1eYmpri6+tLUlISycnJfPvtt1hbWyMn\nJ0fNmjUxMjJi/PjxxMTEYGZmRqdOndDW1ubq1auFDNCPZUC/O7/Dhw8zbtw45s+fz7Vr19DX16+Q\njuzsbJydnVFUVGTNmjXl8nRIJBJ+/PFHli9fTs2aNctlUEskErZt28bZs2crNL7PDWVlZSwtLTlw\n4ADOzs7lalOnTh0OHz5MSEgIHTt2LFPexsaGP/74gxs3bqCurk6zZs3KPT5FRUVOnDjBpk2bcHBw\nYMOGDdSvXx91dfVy6/gQTExMMDExoV+/fsDbv4mEhARiYmJISEggOzubjIwMjh8/zuPHj3n69Cnj\nx4+ndevW6Onp0bRpU4YNG8b69evR1tZGS0sLLS0tTE1NpdU11dXVyczM5Pnz56iqqqKkpFSlYVMC\nAhVBIpGwefNm/ve//6GoqCj9v/6uUaykpESHDh2k4WIRERHk5OQUkjExMaFRo0ZcuHChRIM6LCys\nSBpTR0dHVq5cWQ0ze8tXY1DHxcVV2EtRHlq2bPmf11vVOqtK34fqqWz7Fi1asHfv3iLX39149O7P\nrVq1IiAgQHrt/X/btGnDjh07pEZtwT2JRIKtrS2+vr5IJBLy8/Pp1KkTf/zxR6FXXV26dGHdunUl\nnufl5WFra0vv3r2lemfOnImjoyP5+flkZWWRkZHBihUr6Ny5MyoqKqiqqjJ58mS2b9+OpqYmmpqa\n1KhRA1NTU8zNzWnTpg01atRAQ0ODjIyMQh5IsVhcyAMgFovL7c2tDjIzM+ncuTPx8fGsW7cOfX39\nChvTEokEFxcXZGVl2b17N/Ly8uVq9+DBA+rVq8d3331X7r5evHiBpaUlDRo0qNAYP0fc3d3ZsGFD\nuQ1qOTk5/vzzT4YPHy41ksuif//+BAUF0b17d44fP46VlVW5xycjI4O7uzt9+/bl9OnTmJubs3z5\ncoYMGVIlm14riqqqKk2aNKFJkyaIxWIWLlxY6L5YLCYvL49Hjx5x584dZGVladmyJUlJSSQmJhIV\nFYWKigpbt24lJSWF1NRUPD096dOnD+np6WRmZqKoqMiyZctwdHREJBJJM6H06tULJSUlrK2t0dTU\nLHNdKbi2fv16ZGRkEIlE2Nvb4+vri0gkkh52dnb4+fkVugZvN5i2adMGf39/6fm7/xbQqlUraaGg\n8vxOrK2tK7WuV8VzqSqflV/Cs/xdnfPnzy9TNigoiAcPHjBy5EgA4uPjAYqsx3p6ekRGRkplZGVl\ni3i69fX1S91zER8fX0Svvr4+SUlJZU+qknw1BrWMjEylizkIej+uzqrS96F6KtteVla2xJjbdxf8\ngp9lZWWlsbPvPzREIhHy8vLo6+sX+8BRVlbG3Nxc+uBTVVWlefPm0gcYgLq6Oq1bt5b2+/45QL16\n9bh06ZJU7+PHj7l16xYyMjIoKiqirKzMjRs3CsWvisVifvzxx0J6xGKx9NVcwXl5U8x9bFJTU1m2\nbBl6enr07t2bcePGScNLKkJ2dja3bt0CqJAxffXqVZ49e8aGDRsqZJzFxcUxevToCo3xc2XAgAFM\nmDCBqKiocrfp2bMnDg4OzJgxg9WrV5erjY6ODmvWrKFbt24cPXq0wuM0MDCgYcOGHD58GDc3N7Zs\n2cJvv/1WYT0fA5FIhKmpKaampojFYrp27Vrovlgs5sSJE4XOX7x4AbxNQ1lQ1fPWrVvSL+qPHj3i\n8uXLpKamEh4eTkZGRrnWFXV1dVq2bEl+fj4SiQRVVVXMzc2lX9wlEgmKiooYGxsXcRjA2xSENWvW\nLHL9XWRlZVFTUytXphSJRFLpdb0qnktV+az8Ep7lFdW5ceNGWrduTZMmTcqU/RRfaAvw9vZmzpw5\nFW731RjUBgYG5SoOUFHEYrH0ldx/VW9V66wqfR+qp7LtK9quLHmxWEzfvn1LvFewG7rgvFevXkVk\nevbsWeJ5wbV3vcQJCQlVkr/5cyQ7O5tNmzYxf/58OnfuTM+ePWnbtm2ldQ0cOBAXFxcCAgLK/eUh\nKyuLYcOGsXTp0gp9zlevXiUjI6NCHu3PGUVFRUaOHMmqVauKfDkrjWXLltG0aVOcnJyoUaNGudo4\nOzsjEono0aNHuUqfF0fLli25cuUKf/75J46Ojvj4+KClpYWZmVml9H1uyMjIoKKigpycXKH/l/Hx\n8dL1oWHDhkD515X3ZYpbn/r06VPseEpb+96Vqcr1tqrbVbWO6tD1OehMTEwkMDCw0FsOAwMD4O3z\n6N0N6AkJCVKPtIGBAXl5eSQlJRXyUsfHx9OhQ4cS+zMwMJB6wIvTWxre3t54e3sXe680Q18IphIQ\nEPgqyMnJIT4+HgsLCwIDAzl06BB+fn6VLtudlZXFwIEDkZOTw9LSskKe+OnTp2Nubl7h0JI1a9Zg\nZGT02Xr9K8PYsWPZuXMneXl55W6jra3Npk2bcHNzq1AWjoEDB/L7779z48aNCr+JKEBeXh4PDw/u\n3buHkpISLVu2ZOzYsTx+/LhS+gQEBGDLli0oKSnh6uoqvVanTh0MDAwKvVHJzMzk3Llz0tCtli1b\nIi8vX0jmyZMnREZG0q5duxL7s7GxISgoqNC1oKCgCu2zqCiCQS0gIPBFk56ezu+//465uTmJiYn4\n+flx7NgxWrRoUWmdWVlZ2NnZoaysjL+/f4VePx47doyAgIAK5UiGt/PYt28fhoaGFR3uZ42RkRGd\nOnUqd+XEAnr06EG/fv2IioqqUGGUAQMGYG5ujqOjIzt27KjocKVoaGhgZmZGZGQkGhoaWFlZERUV\nVaHwFQEBgbehOJs2bWLw4MGFwiVFIhHjx49nyZIl/PPPP9y8eRM3NzfU1dWlFWI1NTUZMWIEkydP\n5t9//5Xu0bGysqJLly5SXZ07d2b69OnScy8vL06dOiVN07po0SKCg4Or9e2fYFALCAh8kbx8+ZKH\nDx9iZmZGUFAQ/v7+NG3aFDs7uw/SGxERwZUrV3B1dcXf379C3uK4uDi+//57/Pz8yvVq8V38/Pyw\nsbEpd4z2l8TQoUOJi4urcDGTpUuXkpGRwR9//FGhdjo6Opw6dYpZs2bh7e39QZUKdXV1Wbx4Mffu\n3UNRURE7OzucnJykexIEBARKJzg4mPv370s3I77L5MmTmTBhAh4eHrRq1YqEhAROnDiBsrKyVMbH\nxwcnJycGDRqEnZ0dGhoaHDx4sJCjIyYmplCIh42NDf7+/mzZsgUrKyu2b99OQEAAjRs3rrZ5Cga1\ngIDAF4NEIiE4OJj//e9/1KtXj6ysLM6dO8f+/fsrHSf9Lnv37qV79+6Ym5szceLECnmmJRIJrq6u\njB07tlzVE98lKysLb2/vCpXT/pL49ttvAfjnn38q1E5JSQlLS0tmzZpFREREhdo2adKECxcucPTo\nUYYMGfLBlQm1tbUxNTUlNjaWLl26MHDgQNq0aUNcXBwpKSkfpFtA4GvG3t6evLw8rK2ti70/e/Zs\n4uLiyMjI4PTp01haWha6r6CgwOrVq3nx4gVv3rzhwIEDRTJHxcbG4uvrW+jagAEDuHPnDllZWdy6\ndUu6DlUXgkEtICDw2ZOQkMDjx4+xsLCQejKioqJo0KBBlaWXmzlzJpMmTeL48ePo6OhUuH1MTAzK\nysqFXjuWlx07dtC0adMPClP5nJGRkaFWrVosX768wt5iZWVl1q9fz8CBAyuc8kpfX5/g4GAAaRn7\nD0VVVRUPDw9iYmLw9vaW5mf//vvvOX/+/AfrFxAQ+DIRDGoBAYHPktzcXDZt2sSgQYNo1KgRGRkZ\nbN26lZs3b+Ll5YWurm6V9BMfH8/169c5d+4cly5dqpRRGxAQwIsXL9i5c2eFq9hJJBLmzZtXLVmK\nPid0dXVJTk7m+PHjFW47cOBAnJ2dKxX/qKyszI4dO9DT06N169YcPny4wjqKQ05Ojh49etC4cWMi\nIyNp3Lgxw4cP58qVK8yePZurV69+UKiJgIDAl4VgUAsICHw2PH78mI0bN+Lq6srFixc5ceIEPXr0\nIDIykgYNGmBjY1Ol+UlPnjxJ8+bN0dDQ4OTJkxXOygFw//59PD09sbS0pGbNmhVun5CQQL169Wjf\nvn2F235pzJo1i7lz51bK0Fy4cCGpqamVyrYhEokwMTEhICAADw8Pxo8fX6XGrr6+Pj///DN3796l\nTp06ZGRk8O2339KqVSvmzZtHSkpKhbKcCAgIfHkIBrWAgMAnIysri1OnTnH//n0aNGhA8+bNCQkJ\nwd7enjZt2hAQEICbm1uV58zOzc1l9uzZDB06lJ07d2JmZoacXMXT8j969AgHBwfmz59fqZLVOTk5\nPHz4kFmzZlW47ZeIs7Mzr1+/rpSXWk5Ojl27dhEXF8fGjRsr1X+HDh0Qi8Xcu3ePq1evEhsbWyk9\nJSErK4uWlhZLly7l/v37LFiwgNTUVKKiotDR0cHJyYlt27aRnZ1dpf0KCAh8egSDWkBA4KORnp7O\nyZMniY6Oxs7ODh0dHaZPn46cnBwBAQEkJiayfft23N3dK2XglofMzEw6depEaGgoV65cwd7evlJ6\ncnNz6dGjB15eXri7u1dKx/bt21FSUiq1QMHXhKys7Ad5qU1NTWnatCmzZ8/m4MGDlRqDlpYWBw8e\nRFdXV1r6ujpCM2RlZenWrRtLly7F2tqayMhInJycCAwM5PLlyzRs2BAXFxc2b94s5LgWEPgKEAxq\ngWolNzeX6dOnU7duXZSVlalbty7r1q0r8vrT29sbY2NjVFRUsLe35/bt24XuZ2Vl8dNPP6Grq4ua\nmhr9+vUjMTGxzP737duHpaUlSkpKNG7cmP3791fp/ARKJicnh9u3b5OQkIC7uzvW1tbo6ury66+/\noqCgwPz583ny5AlhYWGYmprSrFkzZGSqb0nKzc1l2bJlXLlyhb59+3Ls2LFK53zOyMjg5s2bdOnS\nhQkTJlRah7e391dTha+8ODs7k5ycTGBgYKXaKysrs3//fn744QdCQ0MrpUNGRgYTExOOHDnCvHnz\n6N27N1lZWZXSVV709fUZNmwYe/fuxdbWloCAAPr06SPNmR4WFkb//v1ZvHgxZ8+erVBBGwEBgU+P\nYFALVCsLFy5kw4YNrFmzhrt377Jq1Sr27NnDokWLpDJLlixhxYoVrF27lsuXL6Onp0fXrl1JS0uT\nyowfP56///4bf39/zp49S0pKCl5eXqWmwgoNDWXw4MEMHTqUa9euMWTIEJydnYX8sVVMXl4ejx8/\n5tq1a6xatYrIyEhatGiBpqYm/fr1IykpiSZNmkjTHoWGhlK7dm06deqEpqbmRxljbGwsdnZ2HD9+\nnBYtWjB58uRKG+85OTkMGjQIRUVFVq5cWemY7oL8qB/rM/hckJWVZfHixcyZM6fSOlq3bs22bdv4\n9ttvuXHjRqX1WFtbIxaLadeuHREREezYseOjbSRs2rQpQ4cOZc+ePSQmJmJlZYWLiwvx8fH8/PPP\nhIWFYWZmRp8+fZgxYwZBQUFkZmYWWhcFBAQ+HwSDWqBauXz5Mn379qVXr17Url2bPn360L59ey5e\nvAi8zXDg4+PDtGnTcHJyonHjxmzdupXU1FR27twJQHJyMr6+vixfvpzOnTvTvHlz/Pz8uHfvHidP\nniyxbx8fHxwcHJg2bRoWFhZMnz6dTp064ePj81Hm/jWRlpZGcHAwz58/Z+XKlXh5edGnTx8uX76M\nmpoabdu2xdXVlVu3bqGpqcn69et5/vw59+7dw9LSkp9++ol27doVStb/MXj58iXR0dG0bNkSFxcX\ngoKCUFJSqrS+vLw8hg8fTl5eHg0bNqy0UZ6cnMyiRYuYMmVKpcfyJdOrVy9ycnIqXD3xXXr06MHq\n1avp3r07GRkZldajoKDAjBkzaNq0KQsXLqR9+/ZcuHCh0voqg0gkQllZmcGDB+Pj48PFixexs7Mj\nKCgINzc3ZGVlmTNnDteuXUNPTw8jIyM6duzI3bt3mTt3Llu3biUwMJCHDx9+1HELCAj8f6onSFFA\n4P/o0aMHS5Ys4e7du1hYWHD79m3Cw8OZPXs28NZzmJCQgKOjo7RNQUzphQsXcHd3JyIigpycnEIy\nJiYm1KlThwsXLhS6/i5hYWGMGzeu0DVHR0d+//33apjpl4VEIuHq1atkZGSQkpJCUlISGhoa/P33\n3yQmJpKQkEDfvn1xcnKSVp9q0aIFHh4exMbGYmZmhr29PTo6OiQlJRUqJysWi2nevPmnmhrw1ou8\nceNG5syZw5o1a7h9+zYGBgYfpDMvL48ffviBhIQEDh06RGRkZKV1TZw4kZ49e2Jra4tYLP6gcX2J\nyMjIsGnTJsLDw2nWrFmlN50OGjSI9PR0rl27hq6uLubm5pUek5qaGtevX2fHjh24uLjQqVMnPD09\nK62vKjA3N8fc3JwBAwYwd+5cxGIxaWlpPHnyhOjoaDIzM8nMzOTkyZO8fPmS8PBwpk2bRrdu3TA0\nNMTAwAA3Nzd27tyJrq4uOjo66OjooKGhweXLlzE1Na3yDb8CAv9VBINaoFoZO3YsT548oVGjRsjJ\nyZGbm8uPP/7I6NGjAaTG2vvpyvT09IiLi5PKyMrKFinlXLNmTRISEkrsOz4+vohefX39QuVJ3+f5\n8+dlxi5mZ2dLx/bu6+HifpZIJGRlZfHw4UMkEgn5+fnk5eUV+vfNmzdcuXKFvLw86ZGbm0tOTg4i\nkYiDBw+Sk5NDTk4OmpqabN68mezsbLKyssjKysLCwoKAgAAyMzPJyMigY8eOLFiwgDdv3kgPd3d3\nevfuLZ3bzJkz+eOPP1BXV0dDQ4OaNWsyYMAA5OTkaNasGfr6+mhra3Pq1CkMDQ2lnmWxWMyQIUOk\n8xSLxYWM6c+B/fv3M23aNIyNjQkKCiIvL++DjWmJRIKbmxtPnz7l0KFDH+Rpv3DhAidOnCiyT+C/\nRps2bXjy5AlTp04tUuGsInz//fccPXoUBwcHTp48iYWFRaV1ycrKMmzYMGksc3h4OIGBgUyePJka\nNWpUWm9VIiMjQ+3atalduzZisZiFCxcWui8Wixk8eDDPnj0jPj4ekUiErq4uiYmJ3L59m6SkJPr3\n78/vv/9OdHQ08vLyeHt706NHD5SVlVFTU0NdXZ1Ro0YxZ84cVFVVUVZWplu3buzatQslJSXpYWFh\nQXh4OAoKCigoKKCjo8OBAweQl5dHTk4OeXl5RCIRoaGhyMrKFjnS09OJiopCVlYWGRkZRCJRoQPe\n7p958uQJQKHwqpJCrbKzs3n27Fmx90oLz8rOzi712VAeqkJHdej60nR+iXrhKzKoHz58WC1Vxlau\nXPmf11uWztmzZ+Pt7V3svdWrV/PXX3/h7+9P48aNEYvFeHp60rZtW3744YdS+63KfMPlxdnZmbt3\n75Yq8+uvv+Lk5CQ9L2mRL/h5+vTpuLi4IBKJpA+Od//18vJi3bp10oeMnJwcsrKyyMvLM3z4cAIC\nApCXl0deXh4nJyfOnz+PgoICioqKKCoqIpFIUFdXR09PDyUlJbS0tHBxcUFVVRVVVVXU1dXJysri\n4sWLyMvLA2/zPf/000+F5iUWi3F2di50Xrdu3cp/mB8RiURCUFAQT548YdWqVaxYsYLu3bsjEok+\n2AOclZXFnTt3SExM5NChQx/8BeKXX35h3rx5lUqz97VhamrK4cOHuX79Ok2bNq20HgMDA+bNm4e9\nvT3Hjh374HGpqakxf/58wsLC2LhxIw0aNGDChAl07dr1g3V/DAwMDKRfIsViMZMnTy50XywWEx4e\nTm5uLklJSTx8+BCxWExGRgZpaWmkpqaSlZXFsGHDyMjIID09HWVlZbS1tcnIyCA1NZUXL15gYmLC\nxYsXycnJISsri969exMQEEBOTg65ubnk5uYybNgwNmzYUMhhUOBM8PLyYvny5eTl5UkdDhKJpJBz\nYvr06QwcOLBE58W7SCQSfv3112JLTJcVGz9r1iz69etX7s+4unRUh64vTeeXqBe+IoPa1NS0WjaT\niMXi/7zeD9G5YMECZs6ciYuLCwCNGzcmNDSURYsW8cMPP0gX/YSEBExMTKTtEhISpPcMDAzIy8sj\nKSmpkJc6KSmpVM+jgYFBkW+i7+otjoIyxaUhFoulHuryIBaLSy15LBaLSzT6xGIxw4YNK3Tu6upa\nROZdA18sFtO9e/ciMu9+vhUZ/+fO0aNHmTNnDikpKfj4+CAWi6ssW0haWhr9+/dn2LBhBAYGoqio\n+EH6EhISyM7OZujQoVUyvi8dWVlZ5s6di4eHB2fOnPkgXW5ubqioqNClSxcCAgKqZHyKiops3ryZ\nu3fvMmfOHDQ0NDh+/Dienp5fxWZSOTk59PX1iYuLK5LxRiwW4+DgUOi8d+/eRWQK1vaC8/ezt4jF\nYsLCwortvyAneGmIxWKph7o8iMXiEj3UZbX7UM9lVeioDl1fms7PWW9pjj5hU6JAtSKRSIoYNzIy\nMlIDvU6dOhgYGHDixAnp/czMTM6dO0e7du0AaNmyJfLy8oVknjx5woMHD6QyxWFjY0NQUFCha0FB\nQdja2n7wvAQ+LTk5OezcuZOIiAimTJnChAkTuHnzJrq6ulVmTCckJNCpUydMTU2xtLT8YGM6LS2N\n2NhY1qxZU+Hy5F8zI0eOJDU1lX379n2wLhcXF7Zv386tW7f4559/qmB0b7GwsGDnzp00b96cyMhI\n6tWrx5QpU3j69GmV9SEgIPBlIxjUAtXKt99+y+LFizly5AgPHjzgn3/+YceOHVKPqkgkYvz48SxZ\nsoR//vmHmzdv4ubmhrq6Ot999x0AmpqajBgxgsmTJ/Pvv/8iFosZOnQoDRo0oEuXLtK+OnfuzPTp\n06XnXl5enDp1iiVLlhAZGcmiRYsIDg5m/PjxH/dDEKgynj9/zuLFi6lbty4bN26kTp06XLt2jUGD\nBlVpDuuMjAxsbW3p1asXf/75Z5WEH3l6eqKlpUXbtm2rYIRfDzIyMqxZswZPT88qqSDo6OhI06ZN\n8fDw4Pfff6/SN3bKysr4+flx+fJlMjIyaNKkCZGRkVy8ePGjpdsTEBD4PBEMaoFqZeXKlQwaNAgP\nDw8sLS35+eef6d+/PwsWLJDKTJ48mQkTJuDh4UGrVq1ISEjgxIkTqKqqSmV8fHxwcnJi0KBB2NnZ\noaGhgY+PTyFDJyYmptCrHBsbG/z9/aX5frdv305AQACtWrX6OJMXqBIkEgl37twhOjqaBg0aEBUV\nRWBgIKdPn6ZmzZpVHmu/fft2xGIxU6dOZc6cOVWif//+/Zw/f/6DslB8zbRv355Ro0Zx9+7dKjFM\n1dTUOHPmDBs2bGDQoEFVnru5Tp06rF69mpiYGNTU1Pjuu+9o164dhw4dKjU3voCAwNeLYFALVCuq\nqqosX76c2NhY0tPTuX//Ph4eHigoKBSSmz17NnFxcWRkZHD69GksLS0L3VdQUJAWBnnz5g0HDhwo\nku4pNja2SLaAAQMGcOfOHbKysrh161axm1UEPk+ysrJYtGgRDRs2pFu3bsjIyBAVFYWvr2+1pOXL\nz89n3rx5/Prrr1hZWfHjjz9Wid60tDQ8PT3x9fWt1kqQXzozZswgKyuLvXv3Vom++vXrc+nSJVRV\nVXFwcKgS7/f71KhRAxMTE+7du4eXlxeLFi0iNDSU77//nuDgYMG4FhD4DyGs7gICAp8NiYmJ/PHH\nH3Ts2JHw8HAePnzIli1bePjwIXXr1kVXV7da+s3NzcXJyYkjR45w/vz5Qm9HPpSxY8fStWtX2rdv\nX2U6v0YUFBQwNzdn3LhxpW7irQhKSkr4+vrSt29fIiIiyrXpuDLIyMgwePBgzp8/T6tWrfjmm2/w\n8vLC1NSUX375hdTUVCEkREDgK+ejGtR79+6lXbt26OjooKysTMOGDVmwYAE5OTmF5BYuXEitWrVQ\nUVGhY8eOXLt27WMOU0BA4CPy6NEjnj59ioODAw0aNCAkJIQJEybQrl07/vjjD2xsbKo1heKdO3eI\niIigTp06hISEYGRkVGW64+LiuHHjBmvXrq0ynV8zmpqaTJo0iYEDB1aZR1kkEjFz5kwaNmyIq6tr\ntVdKVVBQYNKkSVy7do2jR4+ioKDAnTt3qFu3LpMmTeLs2bPV2r+AgMCn4aMa1C9fvqRLly5s3ryZ\nY8eO8cMPP7BgwQImTpwolVm0aBHz589n2rRpHDp0CDU1Nbp06VJqAQ8BAYEvh9evX/P69WumTp2K\nlZUVLVq0IC0tDS8vL549e8auXbv49ttvqz0PeW5uLj4+PnTo0AEzMzN8fHyKhCJ9CKGhoTx48ICA\ngIAq9Xh/7UyaNAlDQ0MmTJhQpXq1tLSkeaWdnZ3JysqqUv3F8c0337BgwQJat27N/v37UVdXx9PT\nkwsXLjBkyBD8/PyIjo4WvNcCAl8BH9Wgdnd3Z+7cufTr14+OHTsyefJkJk6cyPbt24G36dIWL17M\n9OnTGTt2LA4ODuzZsweRSCR4eAQEvlDy8/MJDw/nt99+w9HRkdq1a/PgwQPk5eVZt24d8fHxWFhY\n0K9fvw+qQFgRoqOjsbOzIzAwkJCQkCIVNT+Uly9fMnDgQBo2bChsRKwgIpGIbdu2ERwczLZt26pU\nt6mpKZcvX+abb74hIiKCrVu3Vqn+0rCyssLb25tr167RsmVLOnTowIEDB+jUqRMNGjTg/v37HDp0\niBcvXny0MQkICFQdnzyGumbNmtKQjwsXLpCamlooUbyKigp9+vTh6NGjiaEVpQAAIABJREFUn2qI\nAgICFSA3N5cTJ04wZ84cbGxsOH/+PG5ubty/f59Ro0bx7NkzmjVrxrx587C1tUVO7uPVl8rNzeXZ\ns2fY2Njw3XffcfLkySIbYD8UiUSCl5cX/fv3p2bNmlWq+7+CpqYmfn5+/PLLL1XuSVZRUWH27NlY\nWVmxZMkSXF1dP3o+aUVFRUaNGsXevXt5/Pgxu3btQl5enpUrV1K/fn1MTEyIiopi+/btggdbQOAL\n4ZMY1Hl5eaSnp3Pu3DnWrFnD6NGjAYiMjERWVraIR6dhw4ZERkZ+iqEKCAiUQk5ODidPnmTlypWM\nHj2aRo0aERYWxoIFC0hPT8fb2xs7Oztu3rzJunXrGDBgwCcLfwgNDcXa2prExET+/fdfxo0bVy1Z\nN2bNmkVkZCQLFy6sct3/JVq0aMGUKVO4fv06L1++rHL9qqqqXL58mTp16mBlZcWTJ08+ShjI+4hE\nIqytralduzb//vsvr1694uTJk6ioqHDgwAEcHBzQ0dHBycmJhw8fsnv3biIjI8nLy/voYxUQECiZ\nT2JQq6qqoqamRocOHbC1tWXp0qUAvHr1CjU1tSKxk1paWqSnp5Obm/sphvtJSU1NZfz48ZiZmaGi\nooKtrS3h4eGFZLy9vTE2NkZFRQV3d3du375d6H5WVhY//fQTurq6qKmp0a9fv3J5ZPbt24elpSVt\n27alcePG7N+/v0rnJvBlIJFIiIuL4/Tp08TFxTF8+HBsbGwwMDDg0qVLzJs3j5iYGBo1asSOHTuw\ns7MjJCSEJUuW0K1bt2qPhS6L7OxsnJ2dcXFx4eeff8bKyoqmTZtWS19bt25l586dHDlyBHV19Wrp\n47/ExIkTqVmzZpVuUnwXVVVVFi5cSGhoKK9evaJRo0YcOXKkyvupCCKRiIYNG2JiYsKePXt49OgR\nN2/exNnZGYlEgr+/P7169UJDQ4Pw8HB69+7NvHnz2L9/P7dv3xa82QICn4iP9671HcLCwkhPT+fi\nxYvMnTuXMWPGsGHDhk8xlM+eH3/8kZs3b7Jt2zZMTEzw8/OjS5cu3L59GyMjI5YsWcKKFSvYunUr\nDRo0YMKECXTt2pW7d++ipqYGwPjx4wkMDMTf35+aNWsyceJEevfuTURERIkeutDQUAYPHszcuXNp\n2LAhd+7cwdnZmfPnz9O6deuP+REIVDMSiYSXL1+SlpbG4cOHuXHjBlFRUTg4ODB8+HBiYmJQVVWl\nQYMGeHp60q5dO9zd3alduzYvXrzA09OzkD6xWPyJZlKY3NxcNm/eTG5uLg0bNmTbtm0oKytX2/he\nv37N5MmTCQ4Orrb0fv9F6tWrh7q6OiNHjmTLli3V0oe5uTlpaWn8+eefuLu7065dO6mj53PA0NCQ\n7777DrFYLC2pnpqaytWrV9HR0SE0NJTNmzdz9+5dPD096d+/P/Xq1cPY2Ji+ffty9+5datWqRa1a\ntTA0NPzEsxEQ+Dr5JAZ1s2bNAKQp9IYPH87kyZPR0tIiLS0NiURSyKv16tUrVFRUSoy19Pb25tmz\nZxgaGtKpUyc6der0MaZR7WRkZPD333/z999/06FDB+BtAZSDBw+yfv165s2bh4+PD9OmTZOW8p47\ndy6Ojo7s3LkTd3d3kpOT8fX1ZcuWLXTu3BkAPz8/TE1NOXnyJI6OjsX27ePjg4ODA9OmTUMsFuPk\n5MTp06fx8fFh586dH+cDEKg0eXl5vH79mpcvX5KSksKbN2/YsmUL9+/fJzk5mTZt2jBu3Dji4uKI\ni4tDSUmJpUuXsm/fPiwsLGjXrh36+vps2bKF+vXro6GhAbw1lt8tqvI5bqDKz8/H39+fWbNmYWJi\nwvz58/Hw8KjWPq9evcrt27fZvXs3jRo1qta+/ovs3LkTBwcHZsyYgbOzc7X106VLF27cuMG8efP4\n5ptv+PPPPzE2Ni5SROpzQF1dHTU1Ndq3by9d/wGuXLlCz549uX//PvHx8cjLy7Nr1y6ePn3KkydP\nSExMxMfHhx9//BEjIyMMDAzo0aMHJ0+epFWrVqioqKCiokJ2djaZmZkoKSl9wlkKCHxagoODy52/\n/pMY1O9S8HB++PAhjRo1Ii8vj+jo6EJx1JGRkaU+pLy9vYs86L8GcnNzycvLQ1FRsdB1JSUlzp8/\nT2xsLAkJCYWMYkVFRTp06MCFCxdwd3cnIiKCnJycQjImJiY0atSICxculGhQh4WFMW7cuELXHB0d\n+f3336twhgL5+fnk5+fz+vVrsrOzC72uzc3N5dWrV/j6+pKfn09eXh7GxsYcPXqU1NRUUlNTSUlJ\nwdHRkYkTJ5KcnMyrV6+YNGkSrVq1Ql1dHW1tbTQ0NPDw8CAoKAhLS0vq1KmDjo4O8+bNw8jICCMj\nI9TU1BCLxYwcOVLa/5f2NyWRSHjx4gXNmjVDRUWFjRs3Ym9v///au++wKK73beD3NjrKItIUBZUA\ndiIoihGsscYWY4u9F6LRRA1GxViwJLYYjZpv1GgSJKYoGGKL2EvEtSIxNmwICqjoIrAw7x/+mJel\nKGaAZcn9ua69YGbOnj0Pu8w8c/bMmVLvMf/777/RuXNnbN68ucKczJc3lpaW2LVrF9566y34+fmV\n6ufS0tISixYtwuTJk3H48GF4eXlh3LhxetO7lmcymQx16tRBnTp1ALz4P/7555/F7TqdDqdPn4aP\njw8SEhKQkJAAhUKBO3fu4LfffkNmZia0Wi0mTJiAVq1aQaFQQK1WIyQkBBMnToSNjQ0qVaoEa2tr\nvP3229i5cyesrKxgbm4OV1dXnDlzBhYWFmjcuLE46UBiYiJkMhmUSiVMTExgampaphcjE/1b+Ttp\n586dW2RZg3+ijx49CgBwc3ODk5MTKlWqhPDwcMycORMAoNVqERERIV64+F9ibW2N5s2bY/78+ahf\nvz4cHBzw448/4sSJE3B3d8f9+/cBoMCUX/b29rh37x4A4P79+1AoFKhSpYpeGQcHh5fO7X3//v0C\n9To4OIivWZiFCxfi0aNHr4yrWbNmer3chY35y7+usDItWrTAd999J24r7Gf+3/M+2rRpgzVr1uit\ny01w8/6e/9GrVy/MmjVLTHKzs7PFk5+8P3U6HbKysjB58mT07NkTWVlZyMzMFH9mZmYiJycHK1as\nQNu2baFSqSCTycRvZ2QyGRYtWoTDhw9DoVBAoVDA1tYWaWlpsLKygrOzM6ytreHg4IBZs2ahcuXK\nsLGxQUpKCjIyMqBQKMS/lUajwYgRI/SWjSlZfpns7Gz8/PPPWLBgAYKCgrBgwQJ07dq1TMZu//PP\nP2jXrh0WLFgAOzu7Un+9/zI7Ozvs27cPERERWLlyJSZNmlSqr+fo6Ig6deogJiYGCxYsgLu7OzZs\n2ABnZ+cSn2axLCmVSpiamur9/2s0GqxcuVKvnEajwfPnz6HVapGSkoL4+HgsXrwYjx8/xqNHj5CW\nlgaVSoXs7GzcvXsX6enpqFKlCo4ePYonT55g1qxZSEtLw+zZs9GtWzdxf5nb85277wsICICJiQlU\nKpXeQ6lUQqlUIigoCOPGjYNSqRT3g7kPuVxe4PcePXpgyZIlkMlkkMvlkMvl4u+5+9fCHm3atMGG\nDRsK7IOL+j3vz1wtWrQQpwHOVdh+qDjrmjVrhh9//LFY72lx93VNmzZFWFhYscoWV2nUaYz1AmWc\nUHfs2BHt27dH3bp1oVAocPToUSxbtgz9+vWDm5sbAGDGjBmYN28e1Go1PDw8sGzZMgBAUFDQS+uO\nj4/Hm2++WeJtXr58eanWO2fOHISEhBRZbsuWLRg+fDiqV68OhUKBJk2aoH///oiJiXlp/Ya4EMzW\n1rZYvQ4qlarAV6jF2cHkXzY1NUWNGjX0thX2M//vuQ9ra2v4+vrqrVMoFHq/590Z5y5bW1tjzJgx\n4nLeHX3uQUChUEClUkGhUODx48c4cOAAVCqVeOAwMTGBiYkJlEolzp49W+SJiEajwdChQ/WW83/l\nnT85fvLkiV4yXVFlZWUhISEBffr0gb29PRYsWIBq1aqV2YlCeno63nnnHcyZMwfDhg0rN2PHK7Jq\n1aqhcePGGDBgAACUelINAK6urtiwYQNmzpyJU6dOwdPTE/369cMnn3wi7n8qKplMBktLS1haWuLh\nw4cF/rc0Gg3eeecdveW+ffsWKFNY5012djbOnj0rzq6SlZWl98jtmHjy5AlWrFghrsv7yE3S83Zu\n2NjY4J133im0c6SwjpXch6WlJerVq/fSDpnc3/P+zMvU1FTvTqvF6Swqap1KpSrWSfrrXISqUqlK\nfCrP0qjTGOsFyjihbtq0KTZt2oSbN29CqVSidu3aWLRokV7v84wZM5CTk4PQ0FAkJyfD19cXe/fu\nfeVFPjVr1iyVq5s1Go1B661Vqxaio6ORnp6OJ0+ewMHBAX379kXt2rXh6OgIAEhMTET16tXF5yQm\nJorbHB0dkZ2djeTkZL1e6vv374vjsgvj6OhYoDc6b72FKe63CPl3wv+WRqNBt27dJD2/c+fO/+p5\nr5O0aTQa8YSRpLt69Sq+++47bN26FXPmzMGmTZvg7+8PmUxWZkltXFwczp07h5CQEL2efyp9pqam\nOHDgANq0aYOsrCx89NFHZfK6rq6uSE1NRVxcHFasWAFvb2907NgRw4cPh06n4xCG15TbIZF7fUZR\n/s3+tqihjK96XpcuXV77efnrkHJMyl9XSRwnjbHO8lzvjBkzitxWptPmffbZZ7hw4QLS0tKQmpqK\n06dPY8KECQV604KDg3H79m1otVocPHgQjRo1Kstmlkvm5uZwcHBAamoq9uzZg+7du8PNzQ2Ojo7Y\ns2ePWC4jIwNHjhxBixYtAABNmjSBSqXSK3Pnzh3ExcWJZQrTvHlz7N27V2/d3r174e/vX8KREb2a\nTqdDcnIyOnbsCH9/f6SlpWHbtm1o2LAhWrZsWabfyMTExKB169Zwc3NjMm0gNWvWxMGDB7FhwwbM\nnj27TF/bwcEBoaGhuHz5Mlq0aIGbN2+idu3aWLJkCZKSksq0LURUfhj8Ton0cnv27EFUVBRu3LiB\nvXv3onXr1vDy8sKwYcMAvJgSb/Hixfj1119x8eJFzJkzB9bW1uJXopUrV8aIESMwbdo07N+/HxqN\nBoMGDUKjRo3Qrl078XXatm2L4OBgcXnSpEn4888/sXjxYty4cQOhoaGIjo7G5MmTy/YPQP9ply9f\nxvz58+Hu7o5bt26hX79+uHXrFpYvXw5fX98yb8/u3bvRqVMnrF271qjH0VYE1atXx+HDh/H777/j\nypUrZX6fAnt7e0yYMAFvvvkmfv75Z1y+fBkeHh6Ii4vDrl27SmXebCIqv5hQl3OPHz9GUFAQvLy8\nMGTIELRq1Qq7d+8We/WnTZuGDz/8EBMmTICvry9SUlKwZ88evbvRrVixAj179kTfvn3RsmVLVKpU\nCREREXq9etevX9cb4tG8eXOEhYVh06ZN6NevH7Zu3Yrw8HCDJDH033Lz5k0sXboUMTExaN++PZKS\nkrBt2zZ4e3tj6NChBWa9KSvr16/HkCFD8Ouvv6JHjx4GaQPps7e3R3R0NDIyMtClSxc8fvzYIO3w\n8fHBxo0b8c8//8DKygqhoaGoVq0axo4diwMHDhikTURUtjjoq5zr06fPK+ddnTNnDubMmQPgxfig\nunXr6m03MTHBqlWrsGrVqiLruHHjRoF1vXv3Ru/evSvUjBBUPqWnp2PRokX45ZdfcP36dfTq1Qv9\n+/dHfHy8ePJoqIv+MjMzcf36dXz++ec4fPiw3pSeZHhWVlaoX78+6tSpA39/f+zYscNgbbGzs0P1\n6tVx5MgRxMfHIywsDFOnTsXIkSOxbt069OzZE61btzZY+4io9LCHmojKnCAIOHfuHD799FN4eHjg\n7NmziI+Px4IFC5CQkID169fDxsbG4LOVXLhwAd7e3nj27BmOHz/OZLqckslkWL16NcaOHYtmzZrh\nzp07Br8Fd82aNTF9+nScOXMGjRs3Rq1atTB37lzY29sjNjYWu3btQkpKikHbSEQlhwk1EZU6QRBw\n6dIlLFu2DJ07d8bRo0fRr18/PH36FD/88AOaN2+OtWvXon379lCpVIZuLgDgzz//RLt27TBjxgw0\naNCgwFzuVL7IZDJMnDgRf/31F+7fv49JkyaVm3HM5ubmmDZtGo4dO4YrV67AxsYGS5YsgaurKxo1\naoQpU6YgNTUVWq3W0E0lon+JCTURlThBEKDRaPDFF1/g0qVLqFq1Krp27YorV65g+PDh8PPzw+XL\nl7FixQo0adLE0M3Vk52djZs3b+L999/HDz/8gEGDBhm6SfQa3Nzc0LhxY1y/fh2tWrVCfHy8oZuk\nx97eHs7Ozjh48CBSUlLw9ddfw9bWFvHx8ahatSqaNWuGjz/+GMnJyXj27Jmhm0tExcQx1EQkSXZ2\nNi5cuIBLly7h9OnTaNCgAQIDA2Fvb48OHTqgQYMGOHfuHKpVqyY+p7zeBOXUqVOYOHEixo8fjzNn\nzrx03nUqv5RKJSIiIrBs2TL4+PggODgYAQEBhm5WAUqlEs2bN0fz5s2h0Wjw8OFDnDp1CocOHcKd\nO3dQtWpV1KhRA02aNEHTpk3RsGFDPHr0CDY2NoZuOhHlw4SaiIotJycHx48fx4ULF3D+/Hk0adIE\nbdq0gaOjIxo0aIAmTZrAzc0N165dE+/ypdFo9JLp8uqzzz7DmjVrsGTJEjRo0IDJtJGTyWSYOnUq\nunTpggkTJsDGxgZVqlRBzZo1Dd20IpmbmyMgIAABAQHQaDRITU3FlStX8Ndff+Gvv/5CpUqV8M47\n78DNzQ316tVD//79ce/ePTRq1Ejv5l5EVPYqxJAPCwsLjB49GrGxsXrrMzIyEBQUhKpVq8LKygrd\nu3fH3bt39cqkpqZi0KBBsLGxgY2NDQYPHlysqZfWrFkDNzc3mJubw8fHB0eOHCnRmIgMRafT4ebN\nm0hNTcVXX32FSZMmoXPnzvD398exY8fwwQcf4Pjx46hduzZcXV0RFxeHy5cvIzw8HNOnT4eNjU2x\nbplbXpw4cQIajQaHDh3CmTNnMHjwYEM3iUqQp6cn9u7diypVqsDX1xerVq0qN2OrX8XU1BQNGjTA\n8OHDsXbtWjRu3BgPHjzAxo0b0bFjR2RmZmLlypXw9vaGl5cX2rZti7i4OCxcuBDbt2/HuXPnkJ2d\nbegwiP4TKkQP9V9//YUPP/wQ7du3x99//w0rKysAL256snPnToSFhcHW1hZTpkxB165dERMTA7n8\nxbnEgAEDcOfOHezevRuCIGDkyJEYNGgQdu7cWeTrbdu2DZMnT8batWvRsmVLfPXVV+jUqRNiY2Ph\n4uJSJjET/VtZWVnIyMjA0aNHER8fj1u3biE+Ph6tWrXCkCFDEBcXBwcHB8ybNw/nz5/HG2+8gXbt\n2qFSpUqwtLREUFCQWJdGozHaG5xcu3YNs2fPxqFDh7B+/Xrs2bNH3C9QxSKXy+Hi4oL9+/dj+vTp\nWL16Nb766ivk5OQY3XtuZmaGJk2aoEmTJtBoNNizZw9ycnJw7tw5JCcnIysrC48ePcL333+PK1eu\nYMyYMejSpQv8/f1hY2MDJycn+Pv7IykpCS4uLjxmEZUQ49qTFKFevXr47LPPkJaWhh9++AHAixui\nfPvtt/j888/Rtm1beHt7Y8uWLTh//jz27dsH4MVd2Hbv3o3169ejWbNm8PPzw7p16xAZGYkrV64U\n+XrLli3DsGHDMGLECHh4eGDVqlVwcnLC2rVryyReovx0Oh2SkpJw6dIlPH78GOHh4Vi9ejXmzp2L\n4OBgXLhwAfXq1YOjoyMsLS2h0Wjw0Ucf4bfffkNycjLq168PtVqN9evX49mzZ7h9+zYaNWqEdevW\nYerUqejWrRsCAgIMPo1dSdDpdPjggw/QrFkz1KlTB5cvX4ajo6PRJVb0+ho0aIDff/8dK1euRHx8\nPJo2bYqTJ08aulmSyeVyeHt7o127dnB0dMSSJUvw66+/4tKlS3jrrbdw+vRp9O3bF/7+/jA1NcWT\nJ0/w+eefo1evXnB0dMSRI0fg7u6OZs2a4YMPPsCsWbNw584dbN68GV988QUCAwPRsWNHyOVybNy4\nUe+1d+zYgc6dO8PR0RE+Pj6Ijo4u0L7s7Gx89NFHcHBwgLW1NXr16qV3IzEAePbsGUaPHo0qVarA\n1tYWI0eOxNOnT18Z+5YtW+Dl5QVra2v4+/sX+KaaqKxUiB5q4MVXY61atcKxY8cwevRoxMTEICsr\nCx06dBDLVK9eHV5eXjh+/Dg6dOiA48ePw8rKCs2bNxfLtGjRApaWljh+/DjeeOONAq+TmZmJM2fO\nYNq0aXrrO3TogGPHjpVegFQhCYKAnJwc8Yr+p0+f4unTp0hLS0NaWhrMzMzwzTffAAASExNRs2ZN\nzJ49G8+fP8ejR4+QkpKCDz/8EL6+vlCr1bCzs8O0adMQGRkJBwcH2NnZwdzcHE5OTggPD4eNjQ0c\nHR1x/vx5jBs3Tq8tFf0GPk+fPsWsWbNQu3ZtaLVa/P3335wK7z+qU6dO0Gg0CAoKQu/eveHq6orQ\n0FBDN6vUuLi4oG/fvuKyRqPB3r17AbzYB50+fRodOnRAUlISTp48Ca1Wi4yMDOzbtw+xsbG4e/cu\nunfvjg0bNmD06NH49NNPUaVKFXz44YdYsWIFnj9/jvr16+PPP/9EZGQkrl+/Dmtra1hbW8Pc3ByD\nBw/G3r17sWzZMlSrVg1z5sxB165dsW7dOrFNgwYNQmxsLH766ScoFAqMHTsWQ4YMwaefflpkXBER\nERg9ejQWLlyIli1bYv369ZgwYQJat24NZ2fn0vuDEhWiwiTUwIvpiO7duwcAuH//PhQKRYEDpoOD\ng3hmfP/+fVStWlVvu0wmg729fYGz51wPHz5EdnZ2ga+5X/YcALh3757e3Qh1Oh2OHj1arLhe5wYF\nOp2uxMdzS6mzsLZnZ2fj8OHDLy1TnHoEQdDrDclfpqjl3J9yuVw8qLzKs2fPkJiYiKSkJHh5eWHz\n5s3IycmBIAhiUpyTk4OsrCxkZWUhMzMTz58/R0ZGBvr27Yvx48fj+fPnSE9PFx9arRZarRbLly9H\nhw4dYGlpCUtLS1hbW8PKygrW1tbo168fTpw4AQCwsbGBr68vRo8eDTMzM1SuXBlqtRqPHj1CZmam\n2MOq0WgwbNgwvfZrNBrUq1evWLFWFNnZ2Xjw4AHS0tLw4Ycf4rvvvkO3bt3QtGlTTJw40dDNo3Jg\nyJAhGDhwILZu3Yrz589j7ty5mDx5MurWrQsnJydDN69MyGQyKJVKeHh4wMPDA2+99RaAF/uMLVu2\niOU0Gg3CwsKwZMkSdO7cGSkpKXj06BEWLlyIzMxMXLhwAX/++SdiY2ORnJwsdgp0794dP/74I+zs\n7DBlyhRotVpxOsCNGzfinXfegVKpxK1bt+Dh4YG5c+fC1NQUarUav/zyC/z9/fHll19CpVJBLpdD\nLpdDJpNBJpPhl19+gZubG549e4bz58+ja9euiIyMxPTp04t1LYRMJhN/VygU2L9/v976vNuLsywI\nAg4dOvRar1uU7OzscnUsf906ixPjq+otbo5UHuoFKlhCDbz6TTTU3bOOHTuG5cuXi8sjRozA//73\nv2I/v7gfztettyzqzN/24cOH49tvv31pmeLUM3ToUGzevPmlZYpalslkGDRoELZu3frK1wVeXH3v\n4OAABwcHmJqawtXVVdyp5+7g5XI5VCoVVCoVTExMYGZmBlNTU5ibm+OLL76AmZkZzM3NYW5uDgsL\nC/H3CxcuIDk5udDX1Wg06N+/v95y/l5kjUZToYcr6HQ6pKWlISMjAxcvXkRqairS0tKQmpqKypUr\nY8eOHUhOTsbDhw/RsWNHDB06FElJSXj48CHUajUWLlwI4MXfqUaNGuV2yj4yDKVSiaFDhyImJgbW\n1tZYsmQJbt26hfv372Pp0qXo27cv7O3tUbVqVfTr1w/h4eFQq9Xi0AQbGxvExMRArVbD2tpaPNGW\nmlCUV6ampqhRo4b4vxQYGAgACAwMREhICD7++GO926vnfsN28eJF2NvbA3hxHK5Xrx4yMjJw4sQJ\n/PjjjwgJCcG3336LrKwspKeni50Rd+/eRcuWLZGZmSn+bXNycpCdnY3ExES0bt0aT58+xZEjR5CU\nlIS6deti165dL+3gym1DXu+//z62bNlSoOOlqPKF5RJDhw4tMCTmVa9blOHDh5e7Y3lx6yyJPKs0\n2lqa9QIVLKFOTEwUp7pydHREdnY2kpOT9XqpExMTxflIHR0d8eDBA706BEFAUlJSkVNm2dnZQaFQ\nIDExscBrv6xH491338W7774rLms0mlI5S9JoNCV+BlrSdWo0Gr0eain15O+Jfd3nF7eHOv/zunXr\n9lrlDTmUInfnlp2dLfaoC4KAjIwMcXt6ejqysrKQlJSEnJwc6HQ6ZGRk4Nq1a9DpdMjOzkZWVhae\nPn2KkydPQqfTISsrC4Ig4PfffxeXMzMzoVarsWHDBnG5Vq1aiIiIQEZGBjIyMpCeno5WrVph+fLl\n4nKvXr0wceJEsddeq9Vi+vTp8PPzg06ng5WVFUJDQ7F69WpUqVIF1tbWsLGxQc+ePZGTk4M6derA\nz88PTk5O2LRpE+zt7WFvbw+VSgWNRoORI0ca7O9PxkEul2Pw4MFiz2buUIh27drhwYMHePDgAczN\nzWFtbY3ExERcvnwZqamp6NGjB1atWoWUlBRxWFHTpk3Fb5wsLCwwY8YMjB07VjyJHjhwIJYuXQoz\nMzOYmJjA3Nwcb731Fnbs2AFTU1OYmJjAw8MDp06dgomJCUxMTKBSqWBra4tdu3ZBqVTCxMQEMpkM\nJ06cgFKpFB9arRb//POPuJyZmYnExETxxN/c3Fz8/y/JpL+oJOrhw4dQKBR6M//IZDI4OzsjJSUF\nLi4uEAQBjo6OaNGihd5zq1WrhszMTAwfPrxAvYmJiZgyZQpGjRr5FewlAAAe90lEQVSlN8f4hAkT\ncPfu3dfet2s0GgwdOvS1nlNYHcXpoS5uXeX9WF5adebWW1o5kpR6X/Y/U2ES6piYGERFRcHKygpR\nUVHimWytWrVgbm4O4EVC8fDhQ6xYsQJr164Ve75sbW1hYmIC4MUY6adPn2Lq1KmYPn06Zs+ejU6d\nOum9Vm7v5oQJE8R1Dx8+hJmZWbHnri2s3pJQGvWWdJ3/pr7CdtazZ89Gx44d/3Udc+bMwdtvv13s\n18sVEhKC9u3bv7Rc3l6OefPmoU2bNoVuA4D58+eLB4T823LHBuYuL1q0SBzzn5sYL126FE2bNhXL\nfP7552jSpIle25YvXw4fHx8ALz6/X3zxBfz8/MSdg7m5ORYsWIAuXbpALpdDqVTi008/xdKlS6FU\nKqFQKKBSqfDBBx9g/fr1UKlUUCqVGDZsGMLCwvR65rt3744TJ07A1NQUKpUKLi4uyMzMhJmZGWxs\nbMSkpG3btjAzM4OZmRmsra2xePFiWFhYiI/ExEQ8fvwYpqamkMlk0Gg0GD9+vN7fWaPRoE+fPnrL\nFXkcOJWd3KEQXl5e8PLyAvDi89W5c2e9chqNBmfOnNFbzsjIwLNnz8ThDffv38fKlSvF4V5KpRKd\nO3dGRkYGMjMzkZ6eDoVCAUEQ8PjxY2RmZqJGjRqIiYlBZmam+OjevTvCwsKQmZmJrKwsDBkyBF9/\n/TV0Op34+OCDD7B06VJxOTg4GN26dROPienp6QgNDYWfnx+AF/uRzZs3F+v/pjg974UNgyhsfV45\nOTmFbn/VcwAU+u3cy/bfly9fLvQmP3PmzHmt40lhSvJYaQzH8tKq0xjrBSpIQn3x4kWEh4eLVytb\nWFgAAGbMmIG9e/dixYoVsLGxQUhICBwdHbFnzx7xH3XgwIFISEjA0qVLIQgCPv74YzRt2hSbNm0C\nAMTHx8PKygojRowQe0N37tyJoKAgTJs2DT4+Pvjuu++wbds2REdHF/sGFvHx8Th79myJ/y1Ko96S\nrvPf1pd/5xofH4/z58//6zpu3ryJixcvFvv1ct24cQNxcXGvLJe7/tq1a7h+/Xqh22QyGf755x/c\nuXOn0G1xcXFISkoSl3N7xfKWuXTpErRarbh8/vx5ZGVliUNScpPRvPPRajSaAnPxajQavW9sNBpN\ngXZrNBqcOnVKbzl/r05hw1R69+5doEzexKSwRDg5ORlmZmYgMjZKpRKVK1dG5cqVAQBpaWl6n2+N\nRqN3wXzuuu7du+st5z1ZzF23a9cuveX8s5RoNBpcvXpVbzl3H5J3Xd7//9IcBlWlShXx2+K8vdRJ\nSUmoVasWgBfXNj18+LDAcx88eABbW9tC67Wzs4NcLi/wLXNqaqo4tKQw7u7uhe73b968+drHk8Lq\nKKljpTEcy0urzvJc78tGIlSIhNrX1xf169fHvn37xH9QAFi/fj0++ugjjBs3Dunp6WjXrh3Cw8P1\n/iA///wzgoKCMGDAAABA9+7dsXr1alSqVAkAkJCQgOvXryMrK0vsfR49ejSys7OxZMkSJCQkoEGD\nBoiKikKTJk2K3eaEhIRSuRNbadRb0nWWVH337t2TNAfy3bt3X7rjLcqdO3de68Yl8fHxUKvVRW5X\nKBTi562wbZaWluKyXC4Xv3HJJZPJoFKp9JYrwvR2RFT+FNWBUFRPtKenJ5RKJQ4cOCCeICQmJiI2\nNhajR48GAPj5+SEtLQ2nT58Wv0mLiYlBWloaGjZsWOjrqVQqNGrUCNHR0ejVq5e4/tSpU+jRo0eR\n7VcqlYXu9+/evSt5Tv179+6V2LHSGI7lpVWnMdYLVJCEOj09HRqNBnXr1tVbb2JiglWrVmHVqlVF\nPtfGxkbvSubC5H61lNe4ceMKTDtGRERUkTx79gxXr17FlStXkJOTg9u3b+P8+fNiz/GjR49w+/Zt\nPH78GDKZDNevX4ednZ14Iae1tTWGDRuGqVOnilN5Tp48GV5eXuKwk7p166JNmzYYNWqUeMH6qFGj\n0L59e71OMh8fH4wZMwajRo0C8OLmbSNGjEDz5s3RsmVLrF27Frdu3cLYsWPL+K9EVEFu7EJEREQl\n7/Tp0/D29kb//v2Rnp6O2bNno3Hjxpg1axaAFzd2adSoEVq1agVBEDBixAg0aNAAa9asEetYuXIl\nunbtip49e8LHxwdKpRIRERF636SFhYXhjTfeQEBAAAICAlCnTh18//33em05c+YMbt26JS6///77\nWLhwIaZPnw53d3dERkZi2bJlqFOnTin/VYgKqhA91ERERFTyAgICkJOTU+SUnUOGDBFnRjlz5oxY\nJu/QDzMzM3z11Vfit8W580mnpqaKZezs7PDDDz9Ap9MBgHgx9O3bt8Uyz549KzC8bcqUKRg/fjxy\ncnKgUCj0rm8hKktMqImIiOhfy02ecxPlosoolS9PORQKxUuv/8h//Ujua+ZOREBkSBzyQUREREQk\nARNqIiIiIiIJmFATEREREUnAhJqIiIiISAIm1EREREREEjChJiIiIiKSgAk1EREREZEETKiJiIiI\niCRgQk1EREREJAETaiIiIiIiCZhQExERERFJwISaiIiIiEgCJtRERERERBIwoSYiIiIikoAJNRER\nERGRBEyoiYiIiIgkYEJNRERERCQBE2oiIiIiIgmYUBMRERERScCEmoiIiIhIAibUREREREQSlGlC\nHR4eji5dusDZ2RnW1tbw8fFBWFhYgXILFy6Ei4sLLCwsEBAQgHPnzpVlM4mIiIiIiq1ME+oVK1ZA\nrVZj1apViIiIQOvWrTFgwACsXr1aLBMaGor58+fjk08+QWRkJKysrNCuXTskJiaWZVOJiIiIiIpF\nWZYvFhkZCVtbW3E5MDAQ9+7dw7JlyzBx4kQ8f/4cixYtQnBwMMaPHw8A8PPzg6urK1avXo158+aV\nZXOJiIiIiF6pTHuo8ybTuRo3box79+4BAI4dO4a0tDS899574nYLCwt069YNUVFRZdZOIiIiIqLi\nMvhFicePH4eHhwcAIC4uDgqFAu7u7nplPD09ERcXZ4jmERERERG9VJkO+chv//792LFjBzZu3AgA\nSE1NhZWVFWQymV45tVoNrVYLnU4HpdKgTSYiIiIi0mOwHuqbN29iwIAB6NGjBwYPHmyoZhARERER\nSWKQ7t6UlBR06tQJbm5u+P7778X1arUaT58+hSAIer3UqampsLCwKLJ3OiQkBAkJCXByckJgYCAC\nAwNLOwQiIiIiqsCio6MRHR1drLJlnlBrtVp07doVOp0OkZGRMDMzE7d5enoiOzsbV69e1RtHHRcX\nBy8vryLrDAkJgUajgbe3d6m2nYiIiIj+G/J30s6dO7fIsmU65EOn06FPnz64du0a/vjjD9jZ2elt\nb9GiBSpVqoTw8HBxnVarRUREBDp16lSWTSUiIiIiKpYy7aEeP348oqKisHLlSjx48AAPHjwQt735\n5pswMzPDjBkzMG/ePKjVanh4eGDZsmUAgKCgoLJsKhERERFRsZRpQr13717IZDJMmjRJb71MJsON\nGzdQo0YNzJgxAzk5OQgNDUVycjJ8fX2xd+9eVK1atSybSkRERERULGWaUN+4caNY5YKDgxEcHFzK\nrSEiIiIiks7gN3YhIiIiIjJmTKiJiIiIiCRgQk1EREREJAETaiIiIiIiCZhQExERERFJwISaiIiI\niEgCJtRERERERBIwoSYiIiIikoAJNRERERGRBEyoiYiIiIgkYEJNRERERCQBE2oiIiIiIgmYUBMR\nERERScCEmoiIiIhIAibUREREREQSMKEmIiIiIpKACTURERERkQRMqImIiIiIJGBCTUREREQkARNq\nIiIiIiIJmFATEREREUnAhJqIiIiISAIm1EREREREEjChJiIiIiKSgAk1EREREZEETKiJiIiIiCRg\nQk1EREREJAETaiIiIiIiCZhQExERERFJwISaiIiIiEgCJtRERERERBIwoSYiIiIikoAJNRERERGR\nBEyoiYiIiIgkYEJNRERERCQBE2oiIiIiIgmYUBMRERERScCEmoiIiIhIAibUREREREQSMKEmIiIi\nIpKACTURERERkQRMqImIiIiIJGBCTUREREQkQZkm1FevXsWYMWPQsGFDKBQKtG7dutByCxcuhIuL\nCywsLBAQEIBz586VZTOJiIiIiIqtTBPq2NhYREVFwcvLCx4eHpDJZAXKhIaGYv78+fjkk08QGRkJ\nKysrtGvXDomJiWXZVCIiIiKiYinThLpbt264desWtm3bhrp16xbY/vz5cyxatAjBwcEYP3482rRp\ng59++gkymQyrV68uy6YSERERERVLmSbUhfVI53Xs2DGkpaXhvffeE9dZWFigW7duiIqKKu3mERER\nERG9tnJ1UWJcXBwUCgXc3d311nt6eiIuLs5ArSIiIiIiKlq5SqhTU1NhZWVVoCdbrVZDq9VCp9MZ\nqGVERERERIUrVwk1EREREZGxURq6AXmp1Wo8ffoUgiDo9VKnpqbCwsICSmXhzQ0JCUFCQgKcnJwQ\nGBiIwMDAMmoxEREREVVE0dHRiI6OLlbZcpVQe3p6Ijs7G1evXtUbRx0XFwcvL68inxcSEgKNRgNv\nb++yaCYRERERVXD5O2nnzp1bZNlyNeSjRYsWqFSpEsLDw8V1Wq0WERER6NSpkwFbRkRERERUuDLt\noU5PT8euXbsAAHfv3kVaWhq2b98OAOjSpQvMzc0xY8YMzJs3D2q1Gh4eHli2bBkAICgoqCybSkRE\nRERULGWaUCcmJopzTOeOkX7vvfcgk8lw48YN1KhRAzNmzEBOTg5CQ0ORnJwMX19f7N27F1WrVi3L\nphIRERERFUuZJtSurq7Iycl5Zbng4GAEBweXQYuIiIiIiKQpV2OoiYiIiIiMDRNqIiIiIiIJmFAT\nEREREUnAhJqIiIiISAIm1EREREREEjChJiIiIiKSgAk1EREREZEETKiJiIiIiCRgQk1EREREJAET\naiIiIiIiCZhQExERERFJwISaiIiIiEgCJtRERERERBIwoSYiIiIikoAJNRERERGRBEyoiYiIiIgk\nYEJNRERERCQBE2oiIiIiIgmYUBMRERERScCEmoiIiIhIAibUREREREQSMKEmIiIiIpKACTURERER\nkQRMqImIiIiIJGBCTUREREQkARNqIiIiIiIJmFATEREREUnAhJqIiIiISAIm1EREREREEjChJiIi\nIiKSgAk1EREREZEETKiJiIiIiCRgQk1EREREJAETaiIiIiIiCZhQExERERFJwISaiIiIiEgCJtRE\nRERERBIwoSYiIiIikoAJNRERERGRBEyoiYiIiIgkYEJNRERERCQBE2oiIiIiIgmYUBMRERERScCE\nmoiIiIhIAibUREREREQSMKEmIiIiIpKACXU5Fx0dbegmlCrGZ9xOnz5t6CaUKr5/xo3vn3Gr6O8f\n46tYmFCXcxX9A8n4jBsP6MaN759x4/tn3BhfxVJhEmoTExPWW0r1lnSdJVWf1Hr+7fNf93mvKv+y\n7fm3FVbWkGUUCkW5ak9plSnv7fu3Zf4r719xypT39hW2zhjfv+Jue50yUspLfV5J11EadRlbncZY\nLwBAMHIVIISXmjNnjqGbUKoYn3FjfMaN8Rk3xmfcGJ/xeVnOKfu/AkarTp06uHbtmqGbQUREREQV\nWKNGjXD27NlCtxl9Qk1EREREZEgVZgw1EREREZEhMKEmIiIiIpKACbUBXb16FWPGjEHDhg2hUCjQ\nunXrQsstXLgQLi4usLCwQEBAAM6dO1fGLf13wsPD0aVLFzg7O8Pa2ho+Pj4ICwsrUM5Y49u+fTta\ntGgBOzs7mJubw9PTEwsWLEBWVpZeOWONL7+7d+/CysoKcrkcWq1Wb5sxxrhp0ybI5fICj/Xr1+uV\nM8bYcul0OixatAju7u4wMzODi4sLpkyZUqCcMcYYGBhY6Psnl8tx8uRJsZwxxpbr+++/h7e3N6yt\nrVG9enUMGTIECQkJBcoZa4y//fYbGjZsCDMzM9SqVQvLly8vtJwxxFeSx/PY2Fi0bdsWlpaWqFat\nGubMmYOcnJzSDuGlihPfmjVr0KVLF1SpUgVyuRwHDx4stK7yGF+JKKMLI6kQO3bsEFxcXIT33ntP\n8PLyElq3bl2gzMKFCwVzc3Phq6++Evbv3y907txZsLOzE+7fv2+AFr+e5s2bCwMHDhR++ukn4cCB\nA8JHH30kyGQy4csvvxTLGHN869atE2bNmiX89ttvQnR0tLB48WLB3NxcmDhxoljGmOPLr3///oKj\no6Mgl8uFZ8+eieuNNcaNGzcKMplMiI6OFk6ePCk+kpKSxDLGGluugQMHCs7OzsL69euFQ4cOCVu3\nbhVmzpypV8ZYY4yNjdV7306cOCF06NBBsLe3F7KzswVBMN7YBEEQfv75Z0EmkwlBQUHCn3/+KWzd\nulVwdXUVvL29hZycHLGcscZ45MgRQSaTCaNGjRL27t0rzJs3T1CpVMKKFSv0yhlLfCV1PE9JSRGc\nnJyE9u3bC/v27RO+/vprwdLSUvj000/LMpwCihOfn5+f0KJFC2HAgAGCTCYTDh48WKBMeY2vJDCh\nNqC8O8XevXsX+ICmp6cLlSpVEubNmyeue/bsmVC1alWj+PAlJycXWDdgwADBzc1NEATjj68wM2fO\nFGxsbARBqFjxHTx4ULC1tRU+//xzQSaTiQm1MceYm1DnPTnIy5hjEwRBiIqKElQqlXD58uUiyxh7\njHllZGQIarVaGD9+vCAIxh9bnz59BB8fH711O3fuFGQymRAXFycIgnHH2KFDB6FVq1Z666ZOnSrY\n2toKmZmZgiAYV3wldTxfuHChYGtrK6SlpYnrlixZIlhYWAhPnjwpxQhe7lXx5XXx4sUiE+ryGl9J\n4JAPA5LJZC/dfuzYMaSlpeG9994T11lYWKBbt26Iiooq7eZJZmtrW2Bd48aNce/ePQDGH19hbG1t\nxSEfFSW+7OxsBAUFYc6cOahSpYretooQo1DEREfGHtu3336Ltm3bwtPTs8gyxh5jXn/88QcePXqE\n/v37A6gYsVWqVElvuXLlygD+/2fWmGM8d+4c2rdvr7euffv2SE1NxYkTJwAYV3wldTyPiorC22+/\nDSsrK3Fd3759kZ6eXuQQirLwqvjyKmqfCpTf+EoCE+pyLC4uDgqFAu7u7nrrPT09ERcXZ6BWSXP8\n+HF4eHgAqDjxZWdnQ6vV4siRI/jyyy8xduxYABUnvq+//hpZWVmYMGFCgW0VIcbatWtDpVLB09NT\nb/y0scd26tQpuLu7Y+LEiahcuTIsLS3Ru3dvvTG4xh5jXmFhYXBxcUHLli0BGH9so0ePxtGjR7Fl\nyxY8efIEV65cwaeffqp3kmTMMT5//rzIOy1evnwZgHHHl19xY/n7778LnATXqFEDFhYW+Pvvv8uk\nraWpIsfHhLocS01NhZWVVYEzQ7VaDa1WC51OZ6CW/Tv79+/Hjh07MHXqVAAVJz5LS0tYWVmhVatW\n8Pf3x5IlSwBUjPiSk5Mxe/ZsLFu2rNDbHBtzjM7Ozpg/fz62bt2KyMhI+Pn5YezYsVixYgUA444N\nABISErBp0yacP38e27Ztw8aNGxETE4OePXuKZYw9xlxarRY7d+7U6/0z9tjatWuHb775BiNHjoSN\njQ08PT2Rk5OD7du3i2WMOcY6dergr7/+0lt36tQpAEBKSgoA444vv+LGkpqaChsbmwLPV6vVSE1N\nLZO2lqaKHJ/S0A2g/4abN29iwIAB6NGjBwYPHmzo5pSoEydOQKvV4uTJk/jss88wbtw4rFu3ztDN\nKhEzZ85E8+bN0bFjR0M3pcR16NABHTp0EJfffvttPH/+HAsWLMCkSZMM2LKSkfu1644dO6BWqwEA\nTk5OCAgIQHR0NAIDAw3YupIVEREBrVYrDveoCHbt2oVRo0ZhypQp6NSpE+7fv4+QkBD07NkT+/bt\ng1xu3P1hY8eOxdixY/HNN9+gd+/eOHXqlDjLh7HHRv9NTKjLMbVajadPn0IQBL2z2tTUVFhYWECp\nNI63LyUlBZ06dYKbmxu+//57cX1Fia9x48YAIE6hN2TIEEybNs3o47t06RI2btyIQ4cO4dGjRwAg\nTpf36NEjyGQyo48xv969eyM8PBzx8fFGH5utrS1q164tJtMA4O/vDxMTE1y6dAmBgYFGH2OusLAw\nuLu748033xTXGXtsM2bMwLvvvovQ0FBxXePGjeHp6YkdO3agZ8+eRh3j8OHDce7cOYwbNw6jR4+G\npaUlFi1ahKCgIDg6OgIw/vcwr+LGolar8fjx4wLPT01N1ftfNlYVOT6eBpZjnp6eyM7OxtWrV/XW\nx8XFwcvLy0Ctej1arRZdu3aFTqdDZGQkzMzMxG0VIb78vL29AQDx8fHw8vIy6vj++ecfZGVloXnz\n5rC1tYWtrS0mTpwIAKhevTomTZpk9DHml3ugk8lkRv/59PLyKnRu17wHdGOPEQAeP36MqKioAr3T\nxh7b9evX0ahRI711b7zxBszNzXH9+nUAxh2jXC7Hl19+iYcPH+LChQtITExEs2bNAAB+fn4AjDu+\n/Iobi6enpziGPNft27eh1WpfeoGxsajI8TGhLsdatGiBSpUqITw8XFyn1WoRERGBTp06GbBlxaPT\n6dCnTx9cu3YNf/zxB+zs7PS2G3t8hTl69CgAwM3NDc2bNzfq+N566y1ER0frPaZPnw7gxZXaH3/8\ncYV7D7dv3w47OzvUrFnT6GPr2rUrLly4gOTkZHHdoUOHkJWVpfetijHHCAC//vorMjMzCyTUxh6b\nq6srzpw5o7fu8uXLSE9Ph6urKwDjjxF4MXNJvXr1YGFhgTVr1sDf3x9vvPEGgIoRX67ixtKpUyfs\n3r0bT58+Fddt27ZNvBGMsavI8SlCQkJCDN2I/6r09HTs2LEDsbGx2L17N1JTU2Fvb4/Y2Fi4ubnB\n3NwcALBgwQKo1Wo8efIEU6ZMwb1797B582ZYWloaOIKXGzt2LMLDwxEaGgobGxvcuXNHfNjb28PU\n1BSA8cbXsWNHJCYm4vHjx7hx4wa+++47zJ07F71798bIkSPFr/CMNT4LCwvUrFlT73Hnzh3s2LED\n69atg6Ojo1HH+O677+L27dt48uQJ4uLiMG/ePISFhWHx4sVo2rSpUccGAA0aNMCmTZsQGRkJBwcH\nnD59GuPHj4efnx+Cg4MBwOhjBF4MjbC2tsbMmTP11ht7bDKZDEuXLkVaWhrkcjmOHz+OcePGoXLl\nyli+fDlUKpVRx3jy5EmEhYVBq9Xi7NmzmD17Nnbt2oVffvkF9vb2AIzrPSyp43n9+vWxbt06HDhw\nAM7Ozti3bx+Cg4MxZcoUvP322+U2PpVKhdOnT+PYsWPQaDTYs2cPXF1d8eDBA6Snp8PZ2blcx1ci\nDDD3Nf2fGzduCDKZTJDJZIJcLhfkcrn4e3x8vFhuwYIFQvXq1QVzc3OhVatWwtmzZw3Y6uJzdXUV\nY8r7qCjxzZo1S6hfv75gZWUl2NjYCE2aNBFWr14t6HQ6vXLGGl9hNm7cWOBOiYJgnDEGBwcLHh4e\ngoWFhWBubi74+PgIW7duLVDOGGPLdfXqVaFz586CpaWloFarhWHDhgmPHj0qUM5YY3zw4IGgUqmE\nxYsXF1nGWGMThBd3Y23UqJFgZWUlVKtWTejXr59w48aNAuWMMcaYmBjB19dXsLKyEipVqiR07dpV\nuHjxYqFljSG+kjyex8bGCm3atBHMzc0FZ2dnYfbs2Xo3VjGE4sQ3dOhQvTK5vw8bNkyvrvIYX0mQ\nCcJLZuAmIiIiIqKX4hhqIiIiIiIJmFATEREREUnAhJqIiIiISAIm1EREREREEjChJiIiIiKSgAk1\nEREREZEETKiJiIiIiCRgQk1EVIEEBgYiKCjI0M0gIvpPYUJNRFSByGQyyGQyQzeDiOg/hQk1ERER\nEZEETKiJiCqw/fv3Q61WY926dRg6dCi6deuGxYsXw8nJCTY2Nvjkk0+Qk5ODWbNmwd7eHk5OTvji\niy8M3WwiIqOiNHQDiIiodGzfvh0jRozA//73P7z77rs4fvw4Dh06BBcXFxw8eBBnzpzBwIEDERMT\nA19fXxw7dgz79+/HuHHj0LZtWzRu3NjQIRARGQWZIAiCoRtBREQlo3Xr1qhfvz4aNGiAadOmYfv2\n7WjXrh0AYOjQoThw4ABu3rwpjrP29fWFTqeDRqMR63Bzc8PEiRMxdepUg8RARGRs2ENNRFSBCIKA\n3377DevWrcPhw4fRrFkzve1169bVu2jRwcEBarVar4yDgwMePHhQJu0lIqoIOIaaiKgCkclkaNSo\nEZydnfHNN98U2K5UKguUV6lUBdbl5OSUajuJiCoSJtRERBVMrVq1cODAAezZswejR4/W28Yp9YiI\nSh4TaiKiCkQQBAiCADc3Nxw4cAB//PEHxowZg9zLZfJfNpNb/lXriIioaBxDTURUgeS9sUutWrUQ\nHR2NwMBAjB07VtxeVPmXrSMioqJxlg8iIiIiIgk45IOIiIiISAIm1EREREREEjChJiIiIiKSgAk1\nEREREZEETKiJiIiIiCRgQk1EREREJAETaiIiIiIiCZhQExERERFJwISaiIiIiEiC/wc4wLbmoV/7\newAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x4945650>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bathy = getField(filename2, 'Depth')\n",
      "\n",
      "plt.rcParams.update({'font.size': 15})\n",
      "\n",
      "fig = plt.figure(figsize=(12,9))\n",
      "\n",
      "plt.plot(xc[::4,::4],yc[::4,::4],linewidth=0.75, linestyle='-', color='0.75')\n",
      "\n",
      "lev = range(200,1200,100)\n",
      "\n",
      "levels = [50,100,150]+lev\n",
      "\n",
      "CS = plt.contour(xc,yc,bathy,levels,colors='k',linewidths=[1,1,2.25,1,1,1,1,1,1,1,1,1,1] )\n",
      "\n",
      "plt.clabel(CS, \n",
      "           inline=1,\n",
      "          fmt='%1.1f',\n",
      "           fontsize=14)\n",
      "\n",
      "#plt.plot(xc[:,80],yc[:,80],linewidth=2.25, linestyle='--', color='r')\n",
      "#plt.plot(xc[:,280],yc[:,280],linewidth=2.25, linestyle='--', color='r')\n",
      "\n",
      "plt.plot(xc[160:-50,50],yc[160:-50,50],linewidth=2.5, linestyle='--', color='r')\n",
      "plt.plot(xc[160:-50,-50],yc[160:-50,-50],linewidth=2.5, linestyle='--', color='r')\n",
      "plt.plot(xc[160,50:-50],yc[160,50:-50],linewidth=2.5, linestyle='--', color='r')\n",
      "plt.plot(xc[-50,50:-50],yc[-50,50:-50],linewidth=2.5, linestyle='--', color='r')\n",
      "\n",
      "\n",
      "#plt.plot(xc[:,280],yc[:,280],linewidth=2.25, linestyle='--', color='r')\n",
      "\n",
      "\n",
      "plt.xticks(np.arange(0,120000,10000), [' ','10','20','30','40','50','60','70','80','90','100','110','120'])\n",
      "plt.yticks(np.arange(0,90000,10000), [' ','10','20','30','40','50','60','70','80','90'])\n",
      "\n",
      "plt.xlabel('km',fontsize=16)\n",
      "plt.ylabel('km',fontsize=16)\n",
      "\n",
      "plt.title('Depth (m)',fontsize=16)\n",
      "\n",
      "#plt.savefig('BoxCanyon.png', bbox_inches='tight')\n",
      "#plt.savefig('Boxcanyon.pdf', bbox_inches='tight')\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "<matplotlib.text.Text at 0x528e250>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Q9PR0MjIymDJlCv/4xz/IysoiIyPD/Fuf9u3bY2Njg42NDeHh4XTr1g0bGxtz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\nU6dO0bNnT/71r3/Rt2/fQu0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PPvoIJyenAuWJjo5mxowZ7Nq1ixo1auQYs3DhQk6cOMFX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\niYiIiIgMxKSaiIiIiMhA/wfwD0hXSdVC1wAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x5280f10>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "zlev = 15             # level 34 corresponds to  162.5m , near shelf break\n",
      "\n",
      "timesc = np.arange(0,len(Time),2)   # These correspond to each day\n",
      "\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ugrid = getField(filename,'U')\n",
      "vgrid = getField(filename,'V')\n",
      "\n",
      "print(np.shape(ugrid))\n",
      "print(np.shape(vgrid))\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(21, 90, 360, 360)\n",
        "(21, 90, 360, 360)\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Get mask from T field (not the best, I know)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "temp = getField(filename, 'Temp')\n",
      "\n",
      "temp0 = np.ma.masked_values(temp, 0)\n",
      "\n",
      "MASK = np.ma.getmask(temp0)\n",
      "\n",
      "\n",
      "# Nice cell center mask\n",
      "hFacC = getField(filename2,'HFacC') #(90,360,360)\n",
      "\n",
      "hFacCmasked = np.ma.masked_values(hFacC, 0)\n",
      "\n",
      "MASKhFacC = np.ma.getmask(hFacCmasked)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#### T controls for plot ####\n",
      "\n",
      "plt.rcParams.update({'font.size':13})\n",
      "\n",
      "colorsTemp = [(245.0/255.0,245/255.0,245./255.0), (255/255.0,20/255.0,0)] #(khaki 1246/255.0,143./255.0  ,orangered2)\n",
      "\n",
      "posTemp = [0, 1] \n",
      " \n",
      "NumLev = 30 # number of levels for contour\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "####  PLOT ####\n",
      "\n",
      "plt.rcParams.update({'font.size':14})\n",
      "\n",
      "kk=1\n",
      "\n",
      "fig45=plt.figure(figsize=(18,48))\n",
      "\n",
      "for tt in timesc :\n",
      "        \n",
      "        ### Temperature run01\n",
      "        plt.subplot(6,2,kk)\n",
      "        \n",
      "        ax = plt.gca()\n",
      "        CS = plt.contour(xc,yc,bathy,[150],colors='k',linewidths=[2.25] )\n",
      "        ax.set_axis_bgcolor((205/255.0, 201/255.0, 201/255.0))\n",
      "        plt.contourf(xc,yc,temp0[tt,zlev,:,:],NumLev)#,cmap=make_cmap(colorsTemp, position=posTemp))\n",
      "        #plt.pcolor(xc,yc,temp0[tt,zlev,:,:])\n",
      "        pl.clim([12,13.5])\n",
      "        plt.ticklabel_format(style='sci', axis='x', scilimits=(0,0))\n",
      "        plt.ticklabel_format(style='sci', axis='y', scilimits=(0,0))\n",
      "        \n",
      "        plt.xlabel('m')\n",
      "        plt.ylabel('m')\n",
      "        \n",
      "        cb = plt.colorbar()\n",
      "        \n",
      "        cb.set_label(r'$^{\\circ}$C',position=(1, 0),rotation=0)\n",
      "       \n",
      "        plt.title(\" depth=%1.1f m,%1.1f days \" % (z[zlev],tt/2.))\n",
      "        \n",
      "        kk=kk+1\n",
      "        "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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icYKIiIiIiIiIFMXiBBEREREREREpisUJIiIiIiIiIlIUixNEREREREREpCgW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\nDA0NIUkSsrKy8ODBA9y9exc3bt7En1e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BKrc5cfshi8CVnTp1ShMmTtTh9HRJUlC9eho2ZIj+p3t3ValS5Zb2XVhYqG3bt2vJ0qU6dPiw\nJCkwMFAzZ8xQq5Ytb3l2oLyQRyo2vq0DuAmnMzM1cOhQezHxSM+e+njVKv2hf/8yuYCUYRgKCwvT\n/LlzNXPGDNXy81Nubq7GTZigD1etuuX9AwCAiu9werqeHTxYh9PT5ebmpoHPPKO/ffSRej366C0X\nE5Lk7u6uzvfeq3eXLdOLEybI29tbWVlZGjZihFJSU8vgDACgOMoJoJQys7I0fMQIffvtt/Ly8tKr\nkydrSnx8ub3FsesDD2jle+8ptE0bmaapWX/5i1a8/365HAsAAFQMhw4f1nOjRunsf/8r3xo1NH/u\nXD0/enSZlBK/5ubmpqeffFLvvvOO7mzYUJcvX9YLcXH6ctOmMj8WAFBOAKWQk5Oj0WPH6tTp0/L2\n9ta8N99Uzx49yv24Af7+WjBvnjp26CBJenPuXH362WflflwAAOB6/vOf/2hMTIxyc3Pl7++vt5cs\nUduoqHI/buPGjfVOYqKaNW2qwsJCxU2apH/u3l3uxwVQuVBOADeQX1CgCRMn6sSJE/L09NQbs2cr\nMiLCacevWrWqZr3+utq1bStJmjZjhnbv2eO04wMAAOudP39eMePG6aefflLNmjW1aP58hYSEOO34\nfn5+WvDWW2ocEqKCggK9MHGivj150mnHB3D7o5wAbmDO3Lnas3evJOnVyZMVFRnp9BmqVKmimTNm\nqEmTJiosLNSLL72kzKwsp88BAACcz2azadLkyfr25El5eXnpjdmzFRIc7PQ5avr6au6bb6p27do6\nf/68xr/wgv1bywDgVlFOANexKTVVH3z0kSRpyKBB6v7gg5bNUr16df111iz51qihn376SZPi45Vf\nUGDZPAAAwDlWvP++tm7bJkl6aeJEtWnd2rJZ6tWtq9l//rM8PDyUcfy4Xp81i28dAFAmKCeAa8jI\nyNArkydLkiLCw/XcsGEWTyTVDwrSlF9m2rd/v/76xhsWTwQAAMrTjp07NW/+fEnSY7166dGePS2e\nSGrTurVixoyRJH2enGx/IQcAbgXlBFCCvLw8xb3y4eWVNQAAIABJREFUii5evKi6devqtWnT5O7u\nbvVYkqR7O3XSsCFDJEmr/vY3beIrvQAAuC2dOXNGk6dMkWmaatmihV7405+sHsmuX9+++p/u3SVJ\nc996S4fT0y2eCEBFRzkBlGDOW2/p2LFjcnd312tTp8q/dm2rR3IwbMgQ+7Uvpk6bpiyuPwEAwG3F\nZrMpYepUnTl7VtWrV9eM6dNVtWpVq8eyMwxDk+Li1OjOO5Wfn6+XJk3i+hMAbgnlBPArKamp+mjV\nKknS8KFDFdqmjcUTFefu7q5XJ0+Wr6+vcs6d08vx8Srg+hMAANw23v/gA23fsUOS9PLEiaofFGTx\nRMX5+Pho+tSp8vDw0LcnT2rm7NlWjwSgAqOcAK7y/alTSpg6VVLRdSYGPfusxRNdW506dZQQHy+p\n6PoTi5YssXgiAABQFr76+uv/u87Eo49aekHuG7mneXON/eX6E599/rk+/ewziycCUFFRTgC/uHjp\nkia8+KLOnz8vPz8/TX/1VZe5zsS1dO7USf1//3tJ0rKkJK4/AQBABfdjdrZefOklFRYW6q7GjTXB\nha4zcS2/79tXXe69V5L0+qxZOnTokMUTAaiIKCcAFX2uc8rUqUo/ckTu7u6aMW2aAgICrB6rVJ4f\nPVq/Cw2VJMUnJOjo0aMWTwQAAH6LS5cuafwLL+jHH39UNR8fzXz9dXl7e1s91g0ZhqEpkyerQYMG\nysvL059eeEE/ZmdbPRaACoZyApD01sKF2rBxoyTpj2PH2i82WRF4enho5owZCgwM1M8//6yxsbFc\nIBMAgArGZrMpPiFB/zpwQIZhaNrUqQpu1MjqsUrtjjvu0F9mzlQ1Hx9l/fCDYsaN4wKZAG4K5QQq\nNdM0tfSdd5T07ruSpCf/93/Vr08fi6e6ebVr19acv/xF1Xx89MOPP2rU2LH64YcfrB4LAACUQmFh\noaZOn64vNm2SJMXGxKhzp04WT3Xzmtx1l2ZMny53d3elp6frj3/6k3Jzc60eC0AFQTmBSquwsFBv\nzJ2rhYsXS5K6dO6sCePGyTAMiyf7be6++27NfP11eXp66sSJExoyfLhOfPut1WMBAIDruHTpkuJe\nflmf/uMfkqT+/fqpf79+Fk/123Xs0EFxL74oSdqzd69Gjhmjs2fPWjwVgIqAcgKV0o/Z2RobG6v3\nV66UJD3YrZv+/Npr8vDwsHiyW9O+XTvN+etf5e3trVOnT+uZQYO0fsMGq8cCAAAlOH7ihAYPG2Z/\nx8Szzzyj2JgYi6e6db0fe0yTX3lFbm5uOnjokPo/84z27N1r9VgAXBzlBCqV3Nxcvf/BB3qqb1/t\n3LVLktSvTx9Nf/VVeXp6Wjxd2WjXtq0WzZ8vf39/XbhwQXGTJil2/HgdOnzY6tEAAICkM2fOaMGi\nRfr9H/6g9CNH5ObmpnF//KPGjh5dYd/B+Wu9HnlEs15/XT6/fOR0+MiRenX6dJ08edLq0QC4KMM0\nTdPqIUpiGIb2/PLHI3CzbDabCgoKlJ+fr0uXLunrf/1L+/bt0z8+/1w5585Jknxr1FDcxIl6sGtX\ni6ctH2fOnNGUadO0bft2+7Z2bdvqvi5dFBEergB/f3l5ecnDw0Pu7u63TRiCNSLatVN5PZ0YhqGE\nMtpXglRuc+L2QxbBrbLZbMrPz1d+fr4uXLigffv3a/eePUpeu1Z5eXmSpHr16mnq5MkKCwuzeNry\n8e3Jk5oUH6+Dv3y9qJubm+6/7z51jI5WeHi4atasKU8PD3l6esrNzY08gltCHqnYXLqcqFKlitVj\nwMVd+dfXNE3ZbDZJRUHgev9aV61aVY/27Knnhg2Tn5+fU+a0imma2vjll0pculT/zsi47torgeDq\nH0mEBNxQozvv1JGjRwkDuO2QRVBav84jV+eSa/GtUUNPPfmkBj7zTIX4utBbUVBQoI8/+URJK1Yo\nMzPzumtLyiNkEZRGpw4d9MWmTeSRCsylP2B/pVEGbpWbm5vuuecede7USb0fe0y1a9e2eiSnMAxD\nD3btqm4PPKAt27bpH59/rr379um///1vsbU3ClHAteRdvmz1CEC5IYugLHl5eal1q1a6/7771OuR\nR1StWjWrR3IKDw8PPf3kk+r9+ONav3691m3YoP1ffaULFy4UW0sewW+VX1Bg9Qi4RU5950RCQoJe\nffVVh21169bVqVOnig9mGJrz1786azRUANdqza9+hd8wDLm5ucnd3V3u7u7y8vKSl5eXateuLb+a\nNZ05rssqKCjQ96dO6eLFi8q/fFkFBQUqKCz8v1d6TFO66hWgktD04mre3t4aPnIkr1SgwihtHiGL\noCSlySNubm4y3Nzk7uYmDw8PeXl6yqtKFQXWqaPq1as7c1yXlZeXp1OnTulSXp7y8/NVUFCgwl/y\niM1mkylJpnnd/2fz/3NcrVatWvrDwIHkkQrM6e+caN68uVJSUuy33d3dr7m2U8eOTpgIqFw8PDzU\n6M47rR4DcAnz58/XkiVLdOLECUlSy5YtNWnSJPXo0aPE9SkpKXrjjTeUlpamnJwcNWnSRH/84x81\naNAgh3WpqakaN26cDh48qKCgIL3wwgt67rnn7PcvX75cgwcPdniMYRi6ePGivLy8yvYkUaLS5hGy\nCFA+qlSpopCQEKvHAFwCeaSI08sJd3d31alTx9mHBQCgmIYNG2rmzJm6++67ZbPZtHz5cj3++ONK\nS0tTaGhosfU7duxQaGioJk6cqHr16mnt2rUaPny4qlatqn79+kmSjh8/rh49emjo0KFauXKltmzZ\nolGjRikgIEBPPPGEfV8+Pj46fvy4wysnFBPOQx4BALgK8kgRp5cTGRkZql+/vqpUqaJ27drptdde\nozUFAFiiV69eDrenTZumhQsX6p///GeJYSAuLs7h9ogRI7Rp0yb9/e9/t4eBRYsWqUGDBpozZ44k\nqVmzZtq1a5dmz57tEAYMw1BAQEBZnxJKiTwCAHAV5JEibs48WPv27ZWUlKR169YpMTFRmZmZ6tCh\ng86ePevMMQAAKKawsFAffvihLl26pM6dO5f6cTk5OapVq5b99o4dO9S9e3eHNd27d9fu3btVWFho\n33bx4kUFBwerYcOGevTRR7V///5bPwmUCnkEAOCqKnMeceo7Jx566CH7761atVJ0dLRCQkKUlJSk\n2NjYYusXJybaf48ID1dkRIRT5gQAXN/uPXu0Z+9eq8coE998842io6OVl5cnb29vrVq1Ss2aNSvV\nY//xj3/oyy+/1Pbt2+3bsrKyFBgY6LAuMDBQBQUFys7OVmBgoJo3b65ly5YpNDRU586d05w5c9Sx\nY0d99dVXatKkSZmeH4q7mTxCFgEA10UeKXK75BFLv0rUx8dHLVu21LFjx0q8/7lhw5w8EQCgNCIj\nIhz+SFuydKmF01zbcUknbrCmefPm+vrrr5WTk6PVq1erb9++2rRpkyIjI6/7uG3btql///6aN2/e\nDdf+Wvv27dW+fXv77Q4dOigsLEzz5s2zv/0SznO9PEIWAQDXRR65vfKIpeXEpUuXdOjQIT3wwANW\njgEAuE2F/PJzRWoJazw9PdW4cWNJUlhYmNLS0jR//nwtW7bsmvvdunWrevbsqalTpzpc9Voq+krK\nzMxMh21ZWVny8PCQv79/iftzc3NTeHi4jh49WprTQhkjjwAAyhN5pHSces2J8ePHa/PmzTp+/Lh2\n7dqlJ598UhcvXtSzzz7rzDEAALimwsJC2Wy2a96/efNm9ejRQ1OmTNHYsWOL3R8dHa0NGzY4bNuw\nYYOioqKu+XW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mzp3ryDYAACimqKhIK1eu1IULF9S+ffsSa1avXq26desqOTlZdevW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\nbc7pAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x4945110>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Unstagger all depths of u and v (this will be useful later when plotting cross-sections)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "u2,v2 = unstagger(ugrid,vgrid)\n",
      "\n",
      "#print(np.shape(u2))\n",
      "#print(np.shape(v2))\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Velocity plots"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "####  PLOT ####\n",
      "\n",
      "plt.rcParams.update({'font.size':14})\n",
      "\n",
      "kk=1\n",
      "\n",
      "fig46=plt.figure(figsize=(18,48))\n",
      "\n",
      "timesc2=[2,4,6,8,10]\n",
      "for tt in timesc2 :\n",
      "        \n",
      "        ### Speed and vel vectors, run01\n",
      "        \n",
      "        plt.subplot(6,2,kk)\n",
      "        ax = plt.gca()\n",
      "        ax.set_axis_bgcolor((205/255.0, 201/255.0, 201/255.0))\n",
      "\n",
      "        umask=np.ma.array(u2[tt,zlev,:,:],mask=MASKhFacC[zlev,:-1,:-1])\n",
      "        vmask=np.ma.array(v2[tt,zlev,:,:],mask=MASKhFacC[zlev,:-1,:-1])\n",
      "        \n",
      "        y_slice = yc[:]#np.arange(0, ny-1)\n",
      "        x_slice = xc[:]#np.arange(0, nx-1)\n",
      "        \n",
      "        arrow_step = 10\n",
      "        y_slice_a = y_slice[::arrow_step,::arrow_step]\n",
      "        x_slice_a = x_slice[::arrow_step,::arrow_step]\n",
      "        \n",
      "        Usliceb = umask[::arrow_step,::arrow_step]\n",
      "        Vsliceb = vmask[::arrow_step,::arrow_step]\n",
      "        \n",
      "        #print(np.shape(Uslice))\n",
      "        #print(np.shape(Vslice))\n",
      "        #print(np.shape(x_slice_a))\n",
      "        #print(np.shape(y_slice_a))\n",
      "        \n",
      "        spd = np.sqrt(umask**2 + vmask**2)\n",
      "        \n",
      "        \n",
      "        pos = [0, 1] # to keep white color on zero\n",
      "        \n",
      "        #colorsSpd = [(245.0/255.0,245/255.0,245./255.0), (71./255.0,60.0/255.0,139.0/255.0)] #(white-ish, Slate blue 4)\n",
      "        \n",
      "        #plt.contourf(xc[:-1,:-1],yc[:-1,:-1],spdb,NumLev,cmap=make_cmap(colorsSpd, position=pos))\n",
      "        plt.pcolor(xc[:-1,:-1],yc[:-1,:-1],spd)\n",
      "        \n",
      "        cb = plt.colorbar()\n",
      "        \n",
      "        cb.set_label('m/s', position=(1, 0),rotation=0)\n",
      "        \n",
      "        plt.quiver(x_slice_a,y_slice_a,Usliceb,Vsliceb,pivot='middle' )\n",
      "        \n",
      "        plt.xlabel('m')\n",
      "        \n",
      "        plt.ylabel('m')\n",
      "        \n",
      "        plt.title(\" Depth=%1.1f m,%1.1f days \" % (z[zlev],tt/2.0))\n",
      "        \n",
      "        kk=kk+1\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "ValueError",
       "evalue": "zero-size array to reduction operation minimum which has no identity",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-15-bded921a77b8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     42\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     43\u001b[0m         \u001b[1;31m#plt.contourf(xc[:-1,:-1],yc[:-1,:-1],spdb,NumLev,cmap=make_cmap(colorsSpd, position=pos))\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 44\u001b[1;33m         \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpcolor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0myc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mspd\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     45\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     46\u001b[0m         \u001b[0mcb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolorbar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/kramosmu/anaconda/lib/python2.7/site-packages/matplotlib/pyplot.pyc\u001b[0m in \u001b[0;36mpcolor\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m   2926\u001b[0m         \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhold\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhold\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2927\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2928\u001b[1;33m         \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpcolor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2929\u001b[0m         \u001b[0mdraw_if_interactive\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2930\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/kramosmu/anaconda/lib/python2.7/site-packages/matplotlib/axes.pyc\u001b[0m in \u001b[0;36mpcolor\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   7611\u001b[0m         \u001b[0mcollection\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_norm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnorm\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   7612\u001b[0m         \u001b[0mcollection\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_clim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 7613\u001b[1;33m         \u001b[0mcollection\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mautoscale_None\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   7614\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgrid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   7615\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/kramosmu/anaconda/lib/python2.7/site-packages/matplotlib/cm.pyc\u001b[0m in \u001b[0;36mautoscale_None\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    331\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_A\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    332\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'You must first set_array for mappable'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 333\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mautoscale_None\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_A\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    334\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchanged\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    335\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/kramosmu/anaconda/lib/python2.7/site-packages/matplotlib/colors.pyc\u001b[0m in \u001b[0;36mautoscale_None\u001b[1;34m(self, A)\u001b[0m\n\u001b[0;32m    944\u001b[0m         \u001b[1;34m' autoscale only None-valued vmin or vmax'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    945\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvmin\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 946\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvmin\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mA\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    947\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvmax\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    948\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvmax\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mA\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/kramosmu/anaconda/lib/python2.7/site-packages/numpy/ma/core.pyc\u001b[0m in \u001b[0;36mmin\u001b[1;34m(obj, axis, out, fill_value)\u001b[0m\n\u001b[0;32m   5890\u001b[0m         \u001b[1;31m# If obj doesn't have a max method,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5891\u001b[0m         \u001b[1;31m# ...or if the method doesn't accept a fill_value argument\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5892\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0masanyarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfill_value\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5893\u001b[0m \u001b[0mmin\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mMaskedArray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5894\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/kramosmu/anaconda/lib/python2.7/site-packages/numpy/ma/core.pyc\u001b[0m in \u001b[0;36mmin\u001b[1;34m(self, axis, out, fill_value)\u001b[0m\n\u001b[0;32m   5060\u001b[0m         \u001b[1;31m# No explicit output\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5061\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mout\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5062\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilled\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfill_value\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mview\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5063\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5064\u001b[0m                 \u001b[1;31m# Set the mask\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/kramosmu/anaconda/lib/python2.7/site-packages/numpy/core/_methods.pyc\u001b[0m in \u001b[0;36m_amin\u001b[1;34m(a, axis, out, keepdims)\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_amin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m     return um.minimum.reduce(a, axis=axis,\n\u001b[1;32m---> 14\u001b[1;33m                             out=out, keepdims=keepdims)\n\u001b[0m\u001b[0;32m     15\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_sum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mValueError\u001b[0m: zero-size array to reduction operation minimum which has no identity"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x5280710>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Line plots across-shelf slice at x = 39.37 km (for T)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.rcParams.update({'font.size':14})\n",
      "\n",
      "kk=1\n",
      "\n",
      "fig45=plt.figure(figsize=(21,60))\n",
      "\n",
      "for tt in timesc :\n",
      "        \n",
      "        ### Upstream speed \n",
      "        \n",
      "        plt.subplot(11,3,kk)\n",
      "        ax = plt.gca()\n",
      "        ax.set_axis_bgcolor((205/255.0, 201/255.0, 201/255.0))\n",
      "        \n",
      "        umask=np.ma.array(u2[tt,:,:,:],mask=MASKhFacC[:,:-1,:-1])\n",
      "        vmask=np.ma.array(v2[tt,:,:,:],mask=MASKhFacC[:,:-1,:-1])\n",
      "        \n",
      "        u2z = umask[:,:,80]\n",
      "        v2z = vmask[:,:,80]\n",
      "        \n",
      "        spdup = np.sqrt(u2z**2 + v2z**2)\n",
      "        \n",
      "        fig1=plt.contourf(yc[:-1,80],z[:],spdup[:,:],NumLev,cmap='rainbow')\n",
      "        \n",
      "        pl.clim([0, 0.4])\n",
      "        \n",
      "        cb = plt.colorbar()\n",
      "        \n",
      "        cb.set_label('m/s', position=(1, 0),rotation=0)\n",
      "        cb.set_clim(0, 0.4)\n",
      "        \n",
      "        if kk>30:\n",
      "            plt.xticks([ 20000,  40000,  60000,  80000], ['20','40','60','80'])\n",
      "            plt.xlabel('Along shelf distance (km)')  \n",
      "        else:\n",
      "            plt.xticks([ 20000,  40000,  60000,  80000], [' ',' ',' ',' '])\n",
      "            plt.xlabel(' ')  \n",
      "        \n",
      "        if kk%3==1:\n",
      "            plt.ylabel('Depth (m) ')  \n",
      "        else:\n",
      "            \n",
      "            plt.ylabel(' ')  \n",
      "        \n",
      "        if kk<=3:\n",
      "            plt.title(\" Upstream speed day %1.1f \" % (tt/2.0))\n",
      "        else:\n",
      "            plt.title(\" day %1.1f\" % (tt/2.0))\n",
      "            \n",
      "        kk=kk+1\n",
      "        \n",
      "        ### Downstream speed \n",
      "        plt.subplot(11,3,kk)\n",
      "        ax = plt.gca()\n",
      "        ax.set_axis_bgcolor((205/255.0, 201/255.0, 201/255.0))\n",
      "        \n",
      "        u2z = umask[:,:,280]\n",
      "        v2z = vmask[:,:,280]\n",
      "        \n",
      "        spddwn = np.sqrt(u2z**2 + v2z**2)\n",
      "       \n",
      "        plt.contourf(yc[:-1,280],z[:],spddwn[:,:],NumLev,cmap='rainbow')\n",
      "        \n",
      "        pl.clim([0,0.4])\n",
      "        \n",
      "        cb = plt.colorbar()\n",
      "        \n",
      "        cb.set_label('m/s', position=(1, 0),rotation=0)\n",
      "        \n",
      "        if kk>30:\n",
      "            plt.xticks([ 20000,  40000,  60000,  80000], ['20','40','60','80'])\n",
      "            plt.xlabel('Along shelf distance (km)')  \n",
      "        else:\n",
      "            plt.xticks([ 20000,  40000,  60000,  80000], [' ',' ',' ',' '])\n",
      "            plt.xlabel(' ')  \n",
      "        \n",
      "        if kk%3==1:\n",
      "            \n",
      "            plt.ylabel('Depth (m) ')  \n",
      "        else:\n",
      "            \n",
      "            plt.ylabel(' ')  \n",
      "        \n",
      "        if kk<=3:\n",
      "            plt.title(\" Downstream speed day %1.1f \" % (tt/2.0))\n",
      "        else:\n",
      "            plt.title(\" day %1.1f\" % (tt/2.0))\n",
      "        \n",
      "        \n",
      "        kk=kk+1\n",
      "        \n",
      "        ###  Speed anomaly\n",
      "        plt.subplot(11,3,kk)\n",
      "        ax = plt.gca()\n",
      "        ax.set_axis_bgcolor((205/255.0, 201/255.0, 201/255.0))\n",
      "        \n",
      "        spdanom=spddwn-spdup\n",
      "        \n",
      "        pos2 = [0,0.5, 1] # to keep white color on zero\n",
      "        \n",
      "        colorsSpd = [(71./255.0,60.0/255.0,139.0/255.0) ,(245.0/255.0,245/255.0,245./255.0),(255/255.0,20/255.0,0) ] #(white-ish, Slate blue 4)\n",
      "        \n",
      "        plt.contourf(yc[:-1,80],z[:],spdanom[:,:],NumLev,cmap=make_cmap(colorsSpd, position=pos2))\n",
      "        \n",
      "        pl.clim([-0.18,0.18])\n",
      "        \n",
      "        cb = plt.colorbar()\n",
      "        \n",
      "        cb.set_label('m/s', position=(1, 0),rotation=0)\n",
      "        \n",
      "        if kk>30:\n",
      "            plt.xticks([ 20000,  40000,  60000,  80000], ['20','40','60','80'])\n",
      "            plt.xlabel('Along shelf distance (km)')  \n",
      "        else:\n",
      "            plt.xticks([ 20000,  40000,  60000,  80000], [' ',' ',' ',' '])\n",
      "            plt.xlabel(' ')  \n",
      "        \n",
      "        if kk%3==1:\n",
      "            \n",
      "            plt.ylabel('Depth (m) ')  \n",
      "        else:\n",
      "            \n",
      "            plt.ylabel(' ')  \n",
      "        \n",
      "        if kk<=3:\n",
      "            plt.title(\"Speed anomaly (dwn-up) day %1.1f\" % (tt/2.0))\n",
      "        else:\n",
      "            plt.title(\" day %1.1f\" % (tt/2.0))\n",
      "        \n",
      "        kk=kk+1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Zoom into jet"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trac = getField(filename3, 'Tr1')\n",
      "\n",
      "plt.rcParams.update({'font.size':15})\n",
      "\n",
      "kk=1\n",
      "\n",
      "fig45=plt.figure(figsize=(21,20))\n",
      "\n",
      "for tt in [0,2,4,6,8,10,12] :\n",
      "        \n",
      "        ### Upstream speed \n",
      "        \n",
      "        plt.subplot(3,3,kk)\n",
      "        ax = plt.gca()\n",
      "        ax.set_axis_bgcolor((205/255.0, 201/255.0, 201/255.0))\n",
      "        \n",
      "        Trmask=np.ma.array(trac[tt,:,:,:],mask=MASKhFacC[:,:,:])\n",
      "       \n",
      "        fig1=plt.contourf(yc[:,250],z[:],Trmask[:,:,250],NumLev,cmap='rainbow')\n",
      "        \n",
      "        #pl.clim([0, 0.4])\n",
      "        \n",
      "        cb = plt.colorbar()\n",
      "        \n",
      "        cb.set_label('mol/l', position=(1, 0),rotation=0)\n",
      "        \n",
      "        \n",
      "       \n",
      "            \n",
      "        kk=kk+1\n",
      "        \n",
      "       "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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       "text": [
        "<matplotlib.figure.Figure at 0xa538e50>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trac = getField(filename3, 'Tr1')\n",
      "\n",
      "plt.rcParams.update({'font.size':15})\n",
      "\n",
      "kk=1\n",
      "\n",
      "fig45=plt.figure(figsize=(21,20))\n",
      "\n",
      "for tt in [0,1,4,6,8,10,12,14,16] :\n",
      "        \n",
      "        ### Upstream speed \n",
      "        \n",
      "        plt.subplot(3,3,kk)\n",
      "        ax = plt.gca()\n",
      "        ax.set_axis_bgcolor((205/255.0, 201/255.0, 201/255.0))\n",
      "        \n",
      "        Trmask=np.ma.array(trac[tt,:,:,:],mask=MASKhFacC[:,:,:])\n",
      "       \n",
      "        fig1=plt.contourf(xc[:,:],yc[:,:],Trmask[30,:,:],30,cmap='rainbow')\n",
      "        \n",
      "        #pl.clim([0, 0.4])\n",
      "        \n",
      "        cb = plt.colorbar()\n",
      "        \n",
      "        cb.set_label('mol/l', position=(1, 0),rotation=0)\n",
      "        \n",
      "        \n",
      "       \n",
      "            \n",
      "        kk=kk+1\n",
      "        "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xc7e3650>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "trac = getField(filename3, 'Tr2')\n",
      "\n",
      "plt.rcParams.update({'font.size':15})\n",
      "\n",
      "kk=1\n",
      "\n",
      "fig45=plt.figure(figsize=(21,20))\n",
      "\n",
      "for tt in [0,2,4,6,8,10,12,14,16] :\n",
      "        \n",
      "        ### Upstream speed \n",
      "        \n",
      "        plt.subplot(3,3,kk)\n",
      "        ax = plt.gca()\n",
      "        ax.set_axis_bgcolor((205/255.0, 201/255.0, 201/255.0))\n",
      "        \n",
      "        Trmask=np.ma.array(trac[tt,:,:,:],mask=MASKhFacC[:,:,:])\n",
      "       \n",
      "        fig1=plt.contourf(xc[:,:],yc[:,:],Trmask[30,:,:],30,cmap='rainbow')\n",
      "        \n",
      "        #pl.clim([0, 0.4])\n",
      "        \n",
      "        cb = plt.colorbar()\n",
      "        \n",
      "        cb.set_label('mol/l', position=(1, 0),rotation=0)\n",
      "        \n",
      "        \n",
      "       \n",
      "            \n",
      "        kk=kk+1\n",
      "        "
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trac = getField(filename3, 'Tr3')\n",
      "\n",
      "plt.rcParams.update({'font.size':15})\n",
      "\n",
      "kk=1\n",
      "\n",
      "fig45=plt.figure(figsize=(21,20))\n",
      "\n",
      "for tt in [0,2,4,6,8,10,12,14,16] :\n",
      "        \n",
      "        ### Upstream speed \n",
      "        \n",
      "        plt.subplot(3,3,kk)\n",
      "        ax = plt.gca()\n",
      "        ax.set_axis_bgcolor((205/255.0, 201/255.0, 201/255.0))\n",
      "        \n",
      "        Trmask=np.ma.array(trac[tt,:,:,:],mask=MASKhFacC[:,:,:])\n",
      "       \n",
      "        fig1=plt.contourf(xc[:,:],yc[:,:],Trmask[30,:,:],30,cmap='rainbow')\n",
      "        \n",
      "        #pl.clim([0, 0.4])\n",
      "        \n",
      "        cb = plt.colorbar()\n",
      "        \n",
      "        cb.set_label('mol/l', position=(1, 0),rotation=0)\n",
      "        \n",
      "        \n",
      "       \n",
      "            \n",
      "        kk=kk+1\n",
      "        "
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}