{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Notebook to prepare NEMO 3.4 Tide Files based on CONCEPTS 110 Tide Files"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "from matplotlib import pylab\n",
      "import netCDF4 as NC\n",
      "from numpy import sin, cos, pi\n",
      "from numpy import arange, zeros"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 102
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Open CONCEPTS Tide Files & Bathymetry File"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fT = NC.Dataset('../../input_files/SubDom_tide_elev.nc','r')\n",
      "fU = NC.Dataset('../../input_files/SubDom_tide_ubar.nc','r')\n",
      "fV = NC.Dataset('../../input_files/SubDom_tide_vbar.nc','r')\n",
      "fB = NC.Dataset('../../input_files/SubDom_bathy_meter_NOBCchancomp.nc','r')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Extract Bathymetric Depths"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Depth = fB.variables['Bathymetry']\n",
      "depth = Depth[:]\n",
      "# masking just causes problems, set depth to 0 on land\n",
      "depth[depth.mask] = 0\n",
      "print depth.shape\n",
      "print depth[180,:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(345, 398)\n",
        "[0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4.0 8.0 10.0 6.0 5.0 9.0 17.0 25.0 49.0\n",
        " 65.0 66.0 73.0 83.0 98.0 116.0 125.0 137.0 140.0 154.0 173.0 185.0 202.0\n",
        " 212.0 220.0 224.0 225.0 228.0 227.0 232.0 230.0 228.0 224.0 224.0 216.0\n",
        " 212.0 210.0 207.0 204.0 201.0 202.0 206.0 203.0 209.0 212.0 198.0 188.0\n",
        " 169.0 155.0 137.0 130.0 117.0 112.0 100.0 78.0 61.0 46.0 37.0 33.0 41.0\n",
        " 25.0 24.0 4.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
        " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
        " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
        " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
        " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
        " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
        " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
        " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 23.0 51.0 53.0 53.0 39.0 4.0 0.0 0.0\n",
        " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
        " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 7.0 25.0 28.0 31.0 36.0 17.0\n",
        " 10.0 0.0 0.0 0.0 0.0 24.0 51.0 74.0 75.0 51.0 26.0 0.0 0.0 0.0 0.0 7.0\n",
        " 15.0 7.0 0.0 0.0 0.0 0.0 4.0 23.0 50.0 72.0 110.0 125.0 174.0 203.0 186.0\n",
        " 172.0 157.0 148.0 142.0 140.0 136.0 134.0 131.0 128.0 127.0 126.0 125.0\n",
        " 125.0 121.0 120.0 121.0 120.0 120.0 120.0 118.0 118.0 117.0 117.0 115.0\n",
        " 115.0 115.0 116.0 117.0 117.0 121.0 128.0 116.0 111.0 108.0 107.0 84.0\n",
        " 94.0 26.0 33.0 35.0 17.0 12.0 8.0 10.0 14.0 16.0 17.0 17.0 21.0 24.0 27.0\n",
        " 29.0 29.0 31.0 31.0 32.0 32.0 32.0 31.0 30.0 20.0 8.0 7.0 6.0 12.0 12.0\n",
        " 7.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
        " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
        " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
        " 0.0 0.0]\n"
       ]
      }
     ],
     "prompt_number": 144
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Find the depths on the u-grid and v-grid along the Northern Boundary.\n",
      "Complicated by the fact the Bathymetry array is masked."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Northern Boundary\n",
      "iindex_N = 345-1\n",
      "jlast = 398-1\n",
      "# u is off-set from T and bathymetry so find values in between\n",
      "depthu_N = zeros((jlast+1), dtype=float)\n",
      "depthu_N[0:jlast-1] = 0.5*(depth[iindex_N,0:jlast-1]+depth[iindex_N,1:jlast])\n",
      "depthu_N[jlast] = depth[iindex_N,jlast]\n",
      "\n",
      "# plot to check        \n",
      "pylab.plot (depthu_N,'x',depth[iindex_N,:],'r+')\n",
      "pylab.xlim((175,205))\n",
      "\n",
      "# now the V points, offset in the other direction\n",
      "depthv_N = zeros((jlast+1), dtype=float)\n",
      "depthv_N = 0.5*(depth[iindex_N,:]+depth[iindex_N-1,:])\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEACAYAAABS29YJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108327ad0>"
       ]
      }
     ],
     "prompt_number": 146
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Western Boundary\n",
      "ilast = 345-1\n",
      "jindex_W = 1-1\n",
      "# First the u-points\n",
      "depthu_W = zeros((ilast+1), dtype=float)\n",
      "depthu_W = 0.5*(depth[:,jindex_W]+depth[:,jindex_W+1])\n",
      "\n",
      "# now V points\n",
      "depthv_W = zeros((ilast+1), dtype=float)\n",
      "depthv_W[0:ilast-1] = 0.5*(depth[0:ilast-1,jindex_W]+depth[1:ilast,jindex_W])\n",
      "depthv_W[ilast] = depth[ilast,jindex_W]\n",
      "\n",
      "\n",
      "# plot to check        \n",
      "pylab.plot (depthv_W,'x',depth[:,jindex_W],'r+')\n",
      "pylab.xlim((250,275))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 147,
       "text": [
        "(250, 275)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEACAYAAABS29YJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngAUIewCwAGEPABYg7AHAAv8fouIiu+ar+WcAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1071e2590>"
       ]
      }
     ],
     "prompt_number": 147
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Extract Tides along the Northern and Western Boundaries"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "etaelev = fT.variables['M2_amp_elev']\n",
      "umax = fU.variables['M2_amp_u']\n",
      "vmax = fV.variables['M2_amp_v']\n",
      "print etaelev.shape, umax.shape, vmax.shape\n",
      "etaphase = fT.variables['M2_phi_elev']\n",
      "uphase = fU.variables['M2_phi_u']\n",
      "vphase = fV.variables['M2_phi_v']\n",
      "\n",
      "etaelev_N = etaelev[iindex_N,:]\n",
      "umax_N = umax[iindex_N,:]/depthu_N   # divide by depth because CONCEPTS uses flux not current\n",
      "vmax_N = vmax[iindex_N,:]/depthv_N\n",
      "etaphase_N = etaphase[iindex_N,:]*pi/180.\n",
      "uphase_N = uphase[iindex_N,:]*pi/180.\n",
      "vphase_N = vphase[iindex_N,:]*pi/180.\n",
      "etaZ1_N = etaelev_N*cos(etaphase_N)\n",
      "etaZ2_N = etaelev_N*sin(etaphase_N)\n",
      "uZ1_N = umax_N*cos(uphase_N)\n",
      "uZ2_N = umax_N*sin(uphase_N)\n",
      "vZ1_N = vmax_N*cos(vphase_N)\n",
      "vZ2_N = vmax_N*sin(vphase_N)\n",
      "print etaZ1_N.size\n",
      "\n",
      "jindex_W = 1-1\n",
      "etaelev_W = etaelev[:,jindex_W]\n",
      "umax_W = umax[:,jindex_W]/depthu_W\n",
      "vmax_W = vmax[:,jindex_W]/depthv_W\n",
      "etaphase_W = etaphase[:,jindex_W]*pi/180.\n",
      "uphase_W = uphase[:,jindex_W]*pi/180.\n",
      "vphase_W = vphase[:,jindex_W]*pi/180.\n",
      "etaZ1_W = etaelev_W*cos(etaphase_W)\n",
      "etaZ2_W = etaelev_W*sin(etaphase_W)\n",
      "uZ1_W = umax_W*cos(uphase_W)\n",
      "uZ2_W = umax_W*sin(uphase_W)\n",
      "vZ1_W = vmax_W*cos(vphase_W)\n",
      "vZ2_W = vmax_W*sin(vphase_W)\n",
      "# pad\n",
      "uZ1_W[185:189+1] = uZ1_W[190]\n",
      "            "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(345, 398) (345, 398) (345, 398)\n",
        "398\n"
       ]
      }
     ],
     "prompt_number": 148
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pylab.plot(umax_N[177:197],'b+',depth[344,177:197]/100.,'rx',umax_N[177:197]/depthu_N[177:197]*20,'g+')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 149,
       "text": [
        "[<matplotlib.lines.Line2D at 0x108331d50>,\n",
        " <matplotlib.lines.Line2D at 0x108331690>,\n",
        " <matplotlib.lines.Line2D at 0x108331dd0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAEACAYAAABMEua6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAIMQ6gBgkP8HMYT/UuSxeHMAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1083259d0>"
       ]
      }
     ],
     "prompt_number": 149
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Test that I got the conversion correct"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "phi = arange(0,6.2,0.1)\n",
      "eta_C = etaelev_W[200]*cos(phi-etaphase_W[200])\n",
      "eta_N = etaZ1_W[200]*cos(phi)+etaZ2_W[200]*sin(phi)\n",
      "pylab.plot(phi,eta_C,'+g',phi,eta_N,'-r')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 150,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10831f910>,\n",
        " <matplotlib.lines.Line2D at 0x108172e90>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEACAYAAAC9Gb03AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nHu7/ASKPO8FmPrdSAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108305090>"
       ]
      }
     ],
     "prompt_number": 150
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Create the NEMO 3.4 File"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nemo = NC.Dataset('JPP_bdytide_M2_grid_T.nc', 'w', format='NETCDF3_CLASSIC')\n",
      "nemo.description = 'Tide data from WebTide via JPP'\n",
      "\n",
      "# dimensions\n",
      "nemo.createDimension('xb', etaZ1_N.size+etaZ1_W.size)\n",
      "nemo.createDimension('yb', 1)\n",
      "\n",
      "# variables\n",
      "xb = nemo.createVariable('xb', 'int32', ('xb',))\n",
      "xb.units = 'non dim'\n",
      "xb.longname = 'counter around boundary'\n",
      "yb = nemo.createVariable('yb', 'int32', ('yb',))\n",
      "yb.units = 'non dim'\n",
      "nbidta = nemo.createVariable('nbidta', 'int32' , ('yb','xb'))\n",
      "nbidta.units = 'non dim'\n",
      "nbidta.longname = 'i grid position'\n",
      "nbjdta = nemo.createVariable('nbjdta', 'int32' , ('yb','xb'))\n",
      "nbjdta.units = 'non dim'\n",
      "nbjdta.longname = 'j grid position'\n",
      "nbrdta = nemo.createVariable('nbrdta', 'int32' , ('yb','xb'))\n",
      "nbrdta.units = 'non dim'\n",
      "z1 = nemo.createVariable('z1','float32',('yb','xb'))\n",
      "z1.units = 'm'\n",
      "z1.longname = 'tidal elevation: cosine'\n",
      "z2 = nemo.createVariable('z2','float32',('yb','xb'))\n",
      "z2.units = 'm'\n",
      "z2.longname = 'tidal elevation: sine'\n",
      "    \n",
      "# data\n",
      "total_length = etaZ1_N.size+etaZ1_W.size\n",
      "xb[:] = arange(1, total_length+1)\n",
      "yb[0] = 1\n",
      "\n",
      "etaZ1_W[etaZ1_W.mask] = 0.\n",
      "etaZ1_N[etaZ1_N.mask] = 0.\n",
      "etaZ2_W[etaZ2_W.mask] = 0.\n",
      "etaZ2_N[etaZ2_N.mask] = 0.\n",
      "\n",
      "   # West Boundary\n",
      "nbidta[0,0:etaZ1_W.size] = 1\n",
      "nbjdta[0,0:etaZ1_W.size] = arange(1,etaZ1_W.size+1)\n",
      "z1[0,0:etaZ1_W.size] = etaZ1_W\n",
      "z2[0,0:etaZ1_W.size] = etaZ2_W\n",
      "   # North Boundary\n",
      "nbidta[0,etaZ1_W.size:] = arange(1,etaZ1_N.size+1)\n",
      "nbjdta[0,etaZ1_W.size:] = iindex_N+1\n",
      "z1[0,etaZ1_W.size:] = etaZ1_N\n",
      "z2[0,etaZ2_W.size:] = etaZ2_N    \n",
      "\n",
      "    # and both\n",
      "nbrdta[:] = 1\n",
      "\n",
      "pylab.plot(nbidta[0,:etaZ1_W.size],nbjdta[0,:etaZ1_W.size],'bo',\n",
      "           nbidta[0,etaZ1_W.size:],nbjdta[0,etaZ1_W.size:],'gx')\n",
      "\n",
      "nemo.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEACAYAAABS29YJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\naLEXEXGA/w9J7OW5poIjdQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1083db690>"
       ]
      }
     ],
     "prompt_number": 151
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "u = NC.Dataset('JPP_bdytide_M2_grid_U.nc', 'w', format='NETCDF3_CLASSIC')\n",
      "u.description = 'Tide data from WebTide via JPP'\n",
      "\n",
      "#dimensions\n",
      "u.createDimension('xb', etaZ1_N.size+etaZ1_W.size)\n",
      "u.createDimension('yb', 1)\n",
      "\n",
      "# variables\n",
      "xb = u.createVariable('xb', 'int32', ('xb',))\n",
      "xb.units = 'non dim'\n",
      "xb.longname = 'counter around boundary'\n",
      "yb = u.createVariable('yb', 'int32', ('yb',))\n",
      "yb.units = 'non dim'\n",
      "nbidta = u.createVariable('nbidta', 'int32' , ('yb','xb'))\n",
      "nbidta.units = 'non dim'\n",
      "nbidta.longname = 'i grid position'\n",
      "nbjdta = u.createVariable('nbjdta', 'int32' , ('yb','xb'))\n",
      "nbjdta.units = 'non dim'\n",
      "nbjdta.longname = 'j grid position'\n",
      "nbrdta = u.createVariable('nbrdta', 'int32' , ('yb','xb'))\n",
      "nbrdta.units = 'non dim'\n",
      "u1 = u.createVariable('u1','float32',('yb','xb'))\n",
      "u1.units = 'm'\n",
      "u1.longname = 'tidal x-velocity: cosine'\n",
      "u2 = u.createVariable('u2','float32',('yb','xb'))\n",
      "u2.units = 'm'\n",
      "u2.longname = 'tidal x-velocity: sine'\n",
      "\n",
      "# data\n",
      "total_length = etaZ1_N.size+etaZ1_W.size\n",
      "xb[:] = arange(1, total_length+1)\n",
      "yb[0] = 1\n",
      "\n",
      "uZ1_W[uZ1_W.mask] = 0.\n",
      "uZ1_N[uZ1_N.mask] = 0.\n",
      "uZ2_W[uZ2_W.mask] = 0.\n",
      "uZ2_N[uZ2_N.mask] = 0.\n",
      "\n",
      "   # West Boundary\n",
      "nbidta[0,0:etaZ1_W.size] = 1\n",
      "nbjdta[0,0:etaZ1_W.size] = arange(1,etaZ1_W.size+1)\n",
      "u1[0,0:etaZ1_W.size] = uZ1_W\n",
      "u2[0,0:etaZ1_W.size] = uZ2_W\n",
      "   # North Boundary\n",
      "nbidta[0,etaZ1_W.size:] = arange(1,etaZ1_N.size+1)\n",
      "nbjdta[0,etaZ1_W.size:] = iindex_N+1\n",
      "u1[0,etaZ1_W.size:] = uZ1_N\n",
      "u2[0,etaZ2_W.size:] = uZ2_N    \n",
      "    # and both\n",
      "nbrdta[:] = 1\n",
      "\n",
      "pylab.plot(nbidta[0,:etaZ1_W.size],nbjdta[0,:etaZ1_W.size],'bo',\n",
      "           nbidta[0,etaZ1_W.size:],nbjdta[0,etaZ1_W.size:],'gx')\n",
      "\n",
      "u.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEACAYAAABS29YJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\naLEXEXGA/w9J7OW5poIjdQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1083c4350>"
       ]
      }
     ],
     "prompt_number": 152
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "v = NC.Dataset('JPP_bdytide_M2_grid_V.nc', 'w', format='NETCDF3_CLASSIC')\n",
      "v.description = 'Tide data from WebTide via JPP'\n",
      "\n",
      "# dimensions\n",
      "v.createDimension('xb', etaZ1_N.size+etaZ1_W.size)\n",
      "v.createDimension('yb', 1)\n",
      "\n",
      "# variables\n",
      "xb = v.createVariable('xb', 'int32', ('xb',))\n",
      "xb.units = 'non dim'\n",
      "xb.longname = 'counter around boundary'\n",
      "yb = v.createVariable('yb', 'int32', ('yb',))\n",
      "yb.units = 'non dim'\n",
      "nbidta = v.createVariable('nbidta', 'int32' , ('yb','xb'))\n",
      "nbidta.units = 'non dim'\n",
      "nbidta.longname = 'i grid position'\n",
      "nbjdta = v.createVariable('nbjdta', 'int32' , ('yb','xb'))\n",
      "nbjdta.units = 'non dim'\n",
      "nbjdta.longname = 'j grid position'\n",
      "nbrdta = v.createVariable('nbrdta', 'int32' , ('yb','xb'))\n",
      "nbrdta.units = 'non dim'\n",
      "v1 = v.createVariable('v1','float32',('yb','xb'))\n",
      "v1.units = 'm'\n",
      "v1.longname = 'tidal y-velocity: cosine'\n",
      "v2 = v.createVariable('v2','float32',('yb','xb'))\n",
      "v2.units = 'm'\n",
      "v2.longname = 'tidal y-velocity: sine'\n",
      "\n",
      "# data\n",
      "total_length = etaZ1_N.size+etaZ1_W.size\n",
      "xb[:] = arange(1, total_length+1)\n",
      "yb[0] = 1\n",
      "\n",
      "vZ1_W[vZ1_W.mask] = 0.\n",
      "vZ1_N[vZ1_N.mask] = 0.\n",
      "vZ2_W[vZ2_W.mask] = 0.\n",
      "vZ2_N[vZ2_N.mask] = 0.\n",
      "\n",
      "   # West Boundary\n",
      "nbidta[0,0:etaZ1_W.size] = 1\n",
      "nbjdta[0,0:etaZ1_W.size] = arange(1,etaZ1_W.size+1)\n",
      "v1[0,0:etaZ1_W.size] = vZ1_W\n",
      "v2[0,0:etaZ1_W.size] = vZ2_W\n",
      "   # North Boundary\n",
      "nbidta[0,etaZ1_W.size:] = arange(1,etaZ1_N.size+1)\n",
      "nbjdta[0,etaZ1_W.size:] = iindex_N+1\n",
      "v1[0,etaZ1_W.size:] = vZ1_N\n",
      "v2[0,etaZ2_W.size:] = vZ2_N    \n",
      "\n",
      "    # and both\n",
      "nbrdta[:] = 1\n",
      "\n",
      "pylab.plot(nbidta[0,:etaZ1_W.size],nbjdta[0,:etaZ1_W.size],'bo',\n",
      "           nbidta[0,etaZ1_W.size:],nbjdta[0,etaZ1_W.size:],'gx')\n",
      "\n",
      "v.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAEACAYAAAC6d6FnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngGGKAACAYYoAAIBhigAAgGHq/wD8SW10MqiIrAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1083c9b50>"
       ]
      }
     ],
     "prompt_number": 158
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 142
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}