{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pylab\n",
    "import matplotlib.pyplot as plt\n",
    "import netCDF4 as nc\n",
    "import numpy as np\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from salishsea_tools import tidetools\n",
    "\n",
    "from salishsea_tools import (nc_tools,viz_tools)\n",
    "\n",
    "import matplotlib.cm as cm\n",
    "\n",
    "from matplotlib import animation\n",
    "\n",
    "from numpy import *\n",
    "from pylab import *\n",
    "import matplotlib.patches as patches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "result = nc.Dataset('/ocean/vdo/MEOPAR/ariane-runs/20160701_20160704_4d/ariane_trajectories_qualitative.nc')\n",
    "grid = nc.Dataset('/data/vdo/MEOPAR/NEMO-forcing/grid/bathy_meter_SalishSea2.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 127.89103898])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.variables['final_x'][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lont=result.variables['traj_lon']\n",
    "latt=result.variables['traj_lat']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 126.])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.variables['init_x'][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 600.])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.variables['init_y'][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.variables['init_z'][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.variables['final_z'][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 606.53775356])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.variables['final_y'][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.5])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.variables['init_t'][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8.1252653])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.variables['final_t'][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "masked_array(data = [-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --\n",
       " -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --],\n",
       "             mask = [ True  True  True  True  True  True  True  True  True  True  True  True\n",
       "  True  True  True  True  True  True  True  True  True  True  True  True\n",
       "  True  True  True  True  True  True  True  True  True  True  True  True\n",
       "  True  True  True  True  True  True  True  True  True  True  True  True],\n",
       "       fill_value = 1e+20)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lont[1:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fc0e5d23828>"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAn0AAAGkCAYAAABEqpO3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0e58a4710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = np.arange(1)\n",
    "\n",
    "# 2D\n",
    "fig, ax = plt.subplots(1,1,figsize=(10,6.6))\n",
    "\n",
    "for N,c in zip(n,['red']):\n",
    "    ax.scatter(lont[1:,N],latt[1:,N],color=c)\n",
    "    ax.scatter(lont[0,N],latt[0,N],color='0.30',marker='s')\n",
    "\n",
    "viz_tools.plot_land_mask(ax,grid,coords='map')\n",
    "ax.set_xlim([-124.9,-124.6])\n",
    "ax.set_ylim([49.2,49.8])\n",
    "\n",
    "ax.set_title('Surface particles')\n",
    "ax.set_xlabel('lon')\n",
    "ax.set_ylabel('lat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3*27"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import xarray as xr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "grid2 = xr.open_dataset('/data/vdo/MEOPAR/NEMO-forcing/grid/bathymetry_201702.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 150)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkAAAAOWCAYAAAAHii2wAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0e5f05cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(1,1,figsize=(20,16))\n",
    "grid2.Bathymetry.plot()\n",
    "viz_tools.set_aspect(ax)\n",
    "ax.plot(125,599,'r*', markersize=15)\n",
    "ax.plot(126.89,605.537, 'y*', markersize=15)\n",
    "ax.set_ylim((550,650))\n",
    "ax.set_xlim((100,150))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "OSError",
     "evalue": "No such file or directory",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-14b5c114563f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/ocean/vdo/MEOPAR/ariane-runs/test/ariane_trajectories_qualitative.nc'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32mnetCDF4/_netCDF4.pyx\u001b[0m in \u001b[0;36mnetCDF4._netCDF4.Dataset.__init__ (netCDF4/_netCDF4.c:13231)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mOSError\u001b[0m: No such file or directory"
     ]
    }
   ],
   "source": [
    "test = nc.Dataset('/ocean/vdo/MEOPAR/ariane-runs/test/ariane_trajectories_qualitative.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 621.09702638])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.variables['final_y'][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 150)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkAAAAOWCAYAAAAHii2wAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0e5d8fc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(1,1,figsize=(20,16))\n",
    "grid2.Bathymetry.plot()\n",
    "viz_tools.set_aspect(ax)\n",
    "ax.plot(125,599,'r*', markersize=15)\n",
    "ax.plot(120.45,620.097, 'y*', markersize=15)\n",
    "ax.set_ylim((550,650))\n",
    "ax.set_xlim((100,150))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test2 = nc.Dataset('/ocean/vdo/MEOPAR/ariane-runs/test2/ariane_trajectories_qualitative.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 609.81507556])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test2.variables['final_y'][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2004336"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2694*744"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68376"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "168*407"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhYAAAOXCAYAAAB43AgaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/wEkcbxuZ9GkUgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0e5754f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(1,1,figsize=(20,16))\n",
    "grid2.Bathymetry.plot()\n",
    "viz_tools.set_aspect(ax)\n",
    "ax.plot(125,599,'r*', markersize=15)\n",
    "ax.plot(124.18,608.815, 'y*', markersize=15)\n",
    "ax.set_ylim((590,635))\n",
    "ax.set_xlim((115,135))\n",
    "plt.grid('on')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(898, 398)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.variables['Bathymetry'][:].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 1, 1, 1, 1, 1, 1], dtype=int8)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mesh_mask=nc.Dataset('/data/vdo/MEOPAR/NEMO-forcing/grid/mesh_mask201702.nc')\n",
    "mesh_mask.variables['tmask'][0,0,600,slice(120,130)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "120 614 1 0.5 1.0\n",
      "120 614 2 0.5 1.0\n",
      "120 614 3 0.5 1.0\n",
      "120 614 4 0.5 1.0\n",
      "120 614 5 0.5 1.0\n",
      "120 614 6 0.5 1.0\n",
      "120 614 7 0.5 1.0\n",
      "120 615 1 0.5 1.0\n",
      "120 615 2 0.5 1.0\n",
      "120 615 3 0.5 1.0\n",
      "120 615 4 0.5 1.0\n",
      "120 615 5 0.5 1.0\n",
      "120 619 1 0.5 1.0\n",
      "120 619 2 0.5 1.0\n",
      "120 619 3 0.5 1.0\n",
      "120 619 4 0.5 1.0\n",
      "120 619 5 0.5 1.0\n",
      "120 619 6 0.5 1.0\n",
      "120 619 7 0.5 1.0\n",
      "120 619 8 0.5 1.0\n",
      "120 619 9 0.5 1.0\n",
      "120 619 10 0.5 1.0\n",
      "120 619 11 0.5 1.0\n",
      "120 619 12 0.5 1.0\n",
      "120 619 13 0.5 1.0\n",
      "120 619 14 0.5 1.0\n",
      "120 620 1 0.5 1.0\n",
      "120 620 2 0.5 1.0\n",
      "120 620 3 0.5 1.0\n",
      "120 620 4 0.5 1.0\n",
      "120 620 5 0.5 1.0\n",
      "120 620 6 0.5 1.0\n",
      "120 620 7 0.5 1.0\n",
      "120 620 8 0.5 1.0\n",
      "120 620 9 0.5 1.0\n",
      "120 620 10 0.5 1.0\n",
      "120 620 11 0.5 1.0\n",
      "120 620 12 0.5 1.0\n",
      "120 620 13 0.5 1.0\n",
      "120 620 14 0.5 1.0\n",
      "120 620 15 0.5 1.0\n",
      "120 620 16 0.5 1.0\n",
      "120 620 17 0.5 1.0\n",
      "120 620 18 0.5 1.0\n",
      "120 620 19 0.5 1.0\n",
      "120 621 1 0.5 1.0\n",
      "120 621 2 0.5 1.0\n",
      "120 621 3 0.5 1.0\n",
      "120 621 4 0.5 1.0\n",
      "120 621 5 0.5 1.0\n",
      "120 621 6 0.5 1.0\n",
      "120 621 7 0.5 1.0\n",
      "120 621 8 0.5 1.0\n",
      "120 621 9 0.5 1.0\n",
      "120 621 10 0.5 1.0\n",
      "120 621 11 0.5 1.0\n",
      "120 621 12 0.5 1.0\n",
      "120 621 13 0.5 1.0\n",
      "120 621 14 0.5 1.0\n",
      "120 621 15 0.5 1.0\n",
      "120 621 16 0.5 1.0\n",
      "120 621 17 0.5 1.0\n",
      "120 621 18 0.5 1.0\n",
      "120 621 19 0.5 1.0\n",
      "120 621 20 0.5 1.0\n",
      "120 622 1 0.5 1.0\n",
      "120 622 2 0.5 1.0\n",
      "120 622 3 0.5 1.0\n",
      "120 622 4 0.5 1.0\n",
      "120 622 5 0.5 1.0\n",
      "120 622 6 0.5 1.0\n",
      "120 622 7 0.5 1.0\n",
      "120 622 8 0.5 1.0\n",
      "120 622 9 0.5 1.0\n",
      "120 622 10 0.5 1.0\n",
      "120 622 11 0.5 1.0\n",
      "120 622 12 0.5 1.0\n",
      "120 622 13 0.5 1.0\n",
      "120 622 14 0.5 1.0\n",
      "120 622 15 0.5 1.0\n",
      "120 622 16 0.5 1.0\n",
      "120 622 17 0.5 1.0\n",
      "120 623 1 0.5 1.0\n",
      "120 623 2 0.5 1.0\n",
      "120 623 3 0.5 1.0\n",
      "120 623 4 0.5 1.0\n",
      "120 623 5 0.5 1.0\n",
      "120 623 6 0.5 1.0\n",
      "120 623 7 0.5 1.0\n",
      "120 623 8 0.5 1.0\n",
      "120 623 9 0.5 1.0\n",
      "120 623 10 0.5 1.0\n",
      "120 623 11 0.5 1.0\n",
      "120 623 12 0.5 1.0\n",
      "120 623 13 0.5 1.0\n",
      "120 623 14 0.5 1.0\n",
      "120 623 15 0.5 1.0\n",
      "120 623 16 0.5 1.0\n",
      "120 623 17 0.5 1.0\n",
      "120 623 18 0.5 1.0\n",
      "120 624 1 0.5 1.0\n",
      "120 624 2 0.5 1.0\n",
      "120 624 3 0.5 1.0\n",
      "120 624 4 0.5 1.0\n",
      "120 624 5 0.5 1.0\n",
      "120 624 6 0.5 1.0\n",
      "120 624 7 0.5 1.0\n",
      "120 624 8 0.5 1.0\n",
      "120 624 9 0.5 1.0\n",
      "120 624 10 0.5 1.0\n",
      "120 624 11 0.5 1.0\n",
      "120 624 12 0.5 1.0\n",
      "120 624 13 0.5 1.0\n",
      "120 624 14 0.5 1.0\n",
      "120 624 15 0.5 1.0\n",
      "120 624 16 0.5 1.0\n",
      "120 624 17 0.5 1.0\n",
      "120 625 1 0.5 1.0\n",
      "120 625 2 0.5 1.0\n",
      "120 625 3 0.5 1.0\n",
      "120 625 4 0.5 1.0\n",
      "120 625 5 0.5 1.0\n",
      "120 625 6 0.5 1.0\n",
      "120 625 7 0.5 1.0\n",
      "120 625 8 0.5 1.0\n",
      "120 625 9 0.5 1.0\n",
      "120 625 10 0.5 1.0\n",
      "120 625 11 0.5 1.0\n",
      "120 625 12 0.5 1.0\n",
      "120 625 13 0.5 1.0\n",
      "120 625 14 0.5 1.0\n",
      "120 625 15 0.5 1.0\n",
      "120 625 16 0.5 1.0\n",
      "120 625 17 0.5 1.0\n",
      "120 625 18 0.5 1.0\n",
      "120 626 1 0.5 1.0\n",
      "120 626 2 0.5 1.0\n",
      "120 626 3 0.5 1.0\n",
      "120 626 4 0.5 1.0\n",
      "120 626 5 0.5 1.0\n",
      "120 626 6 0.5 1.0\n",
      "120 626 7 0.5 1.0\n",
      "120 626 8 0.5 1.0\n",
      "120 626 9 0.5 1.0\n",
      "120 626 10 0.5 1.0\n",
      "120 626 11 0.5 1.0\n",
      "120 626 12 0.5 1.0\n",
      "120 627 1 0.5 1.0\n",
      "120 627 2 0.5 1.0\n",
      "120 627 3 0.5 1.0\n",
      "120 627 4 0.5 1.0\n",
      "120 627 5 0.5 1.0\n",
      "120 627 6 0.5 1.0\n",
      "120 627 7 0.5 1.0\n",
      "120 627 8 0.5 1.0\n",
      "120 627 9 0.5 1.0\n",
      "120 627 10 0.5 1.0\n",
      "120 627 11 0.5 1.0\n",
      "120 627 12 0.5 1.0\n",
      "121 603 1 0.5 1.0\n",
      "121 603 2 0.5 1.0\n",
      "121 603 3 0.5 1.0\n",
      "121 604 1 0.5 1.0\n",
      "121 604 2 0.5 1.0\n",
      "121 604 3 0.5 1.0\n",
      "121 606 1 0.5 1.0\n",
      "121 606 2 0.5 1.0\n",
      "121 606 3 0.5 1.0\n",
      "121 613 1 0.5 1.0\n",
      "121 613 2 0.5 1.0\n",
      "121 613 3 0.5 1.0\n",
      "121 613 4 0.5 1.0\n",
      "121 613 5 0.5 1.0\n",
      "121 613 6 0.5 1.0\n",
      "121 613 7 0.5 1.0\n",
      "121 614 1 0.5 1.0\n",
      "121 614 2 0.5 1.0\n",
      "121 614 3 0.5 1.0\n",
      "121 614 4 0.5 1.0\n",
      "121 614 5 0.5 1.0\n",
      "121 614 6 0.5 1.0\n",
      "121 614 7 0.5 1.0\n",
      "121 614 8 0.5 1.0\n",
      "121 614 9 0.5 1.0\n",
      "121 614 10 0.5 1.0\n",
      "121 614 11 0.5 1.0\n",
      "121 614 12 0.5 1.0\n",
      "121 615 1 0.5 1.0\n",
      "121 615 2 0.5 1.0\n",
      "121 615 3 0.5 1.0\n",
      "121 615 4 0.5 1.0\n",
      "121 615 5 0.5 1.0\n",
      "121 615 6 0.5 1.0\n",
      "121 615 7 0.5 1.0\n",
      "121 615 8 0.5 1.0\n",
      "121 615 9 0.5 1.0\n",
      "121 615 10 0.5 1.0\n",
      "121 615 11 0.5 1.0\n",
      "121 616 1 0.5 1.0\n",
      "121 616 2 0.5 1.0\n",
      "121 616 3 0.5 1.0\n",
      "121 616 4 0.5 1.0\n",
      "121 616 5 0.5 1.0\n",
      "121 616 6 0.5 1.0\n",
      "121 616 7 0.5 1.0\n",
      "121 616 8 0.5 1.0\n",
      "121 618 1 0.5 1.0\n",
      "121 618 2 0.5 1.0\n",
      "121 618 3 0.5 1.0\n",
      "121 618 4 0.5 1.0\n",
      "121 618 5 0.5 1.0\n",
      "121 618 6 0.5 1.0\n",
      "121 618 7 0.5 1.0\n",
      "121 618 8 0.5 1.0\n",
      "121 618 9 0.5 1.0\n",
      "121 618 10 0.5 1.0\n",
      "121 618 11 0.5 1.0\n",
      "121 618 12 0.5 1.0\n",
      "121 618 13 0.5 1.0\n",
      "121 618 14 0.5 1.0\n",
      "121 618 15 0.5 1.0\n",
      "121 618 16 0.5 1.0\n",
      "121 619 1 0.5 1.0\n",
      "121 619 2 0.5 1.0\n",
      "121 619 3 0.5 1.0\n",
      "121 619 4 0.5 1.0\n",
      "121 619 5 0.5 1.0\n",
      "121 619 6 0.5 1.0\n",
      "121 619 7 0.5 1.0\n",
      "121 619 8 0.5 1.0\n",
      "121 619 9 0.5 1.0\n",
      "121 619 10 0.5 1.0\n",
      "121 619 11 0.5 1.0\n",
      "121 619 12 0.5 1.0\n",
      "121 619 13 0.5 1.0\n",
      "121 619 14 0.5 1.0\n",
      "121 619 15 0.5 1.0\n",
      "121 619 16 0.5 1.0\n",
      "121 619 17 0.5 1.0\n",
      "121 619 18 0.5 1.0\n",
      "121 619 19 0.5 1.0\n",
      "121 619 20 0.5 1.0\n",
      "121 620 1 0.5 1.0\n",
      "121 620 2 0.5 1.0\n",
      "121 620 3 0.5 1.0\n",
      "121 620 4 0.5 1.0\n",
      "121 620 5 0.5 1.0\n",
      "121 620 6 0.5 1.0\n",
      "121 620 7 0.5 1.0\n",
      "121 620 8 0.5 1.0\n",
      "121 620 9 0.5 1.0\n",
      "121 620 10 0.5 1.0\n",
      "121 620 11 0.5 1.0\n",
      "121 620 12 0.5 1.0\n",
      "121 620 13 0.5 1.0\n",
      "121 620 14 0.5 1.0\n",
      "121 620 15 0.5 1.0\n",
      "121 620 16 0.5 1.0\n",
      "121 620 17 0.5 1.0\n",
      "121 620 18 0.5 1.0\n",
      "121 620 19 0.5 1.0\n",
      "121 620 20 0.5 1.0\n",
      "121 620 21 0.5 1.0\n",
      "121 621 1 0.5 1.0\n",
      "121 621 2 0.5 1.0\n",
      "121 621 3 0.5 1.0\n",
      "121 621 4 0.5 1.0\n",
      "121 621 5 0.5 1.0\n",
      "121 621 6 0.5 1.0\n",
      "121 621 7 0.5 1.0\n",
      "121 621 8 0.5 1.0\n",
      "121 621 9 0.5 1.0\n",
      "121 621 10 0.5 1.0\n",
      "121 621 11 0.5 1.0\n",
      "121 621 12 0.5 1.0\n",
      "121 621 13 0.5 1.0\n",
      "121 621 14 0.5 1.0\n",
      "121 621 15 0.5 1.0\n",
      "121 621 16 0.5 1.0\n",
      "121 621 17 0.5 1.0\n",
      "121 621 18 0.5 1.0\n",
      "121 622 1 0.5 1.0\n",
      "121 622 2 0.5 1.0\n",
      "121 622 3 0.5 1.0\n",
      "121 622 4 0.5 1.0\n",
      "121 622 5 0.5 1.0\n",
      "121 622 6 0.5 1.0\n",
      "121 622 7 0.5 1.0\n",
      "121 622 8 0.5 1.0\n",
      "121 622 9 0.5 1.0\n",
      "121 622 10 0.5 1.0\n",
      "121 622 11 0.5 1.0\n",
      "121 622 12 0.5 1.0\n",
      "121 622 13 0.5 1.0\n",
      "121 622 14 0.5 1.0\n",
      "121 622 15 0.5 1.0\n",
      "121 622 16 0.5 1.0\n",
      "121 622 17 0.5 1.0\n",
      "121 623 1 0.5 1.0\n",
      "121 623 2 0.5 1.0\n",
      "121 623 3 0.5 1.0\n",
      "121 623 4 0.5 1.0\n",
      "121 623 5 0.5 1.0\n",
      "121 623 6 0.5 1.0\n",
      "121 623 7 0.5 1.0\n",
      "121 623 8 0.5 1.0\n",
      "121 623 9 0.5 1.0\n",
      "121 623 10 0.5 1.0\n",
      "121 623 11 0.5 1.0\n",
      "121 623 12 0.5 1.0\n",
      "121 623 13 0.5 1.0\n",
      "121 623 14 0.5 1.0\n",
      "121 623 15 0.5 1.0\n",
      "121 623 16 0.5 1.0\n",
      "121 623 17 0.5 1.0\n",
      "121 624 1 0.5 1.0\n",
      "121 624 2 0.5 1.0\n",
      "121 624 3 0.5 1.0\n",
      "121 624 4 0.5 1.0\n",
      "121 624 5 0.5 1.0\n",
      "121 624 6 0.5 1.0\n",
      "121 624 7 0.5 1.0\n",
      "121 624 8 0.5 1.0\n",
      "121 624 9 0.5 1.0\n",
      "121 624 10 0.5 1.0\n",
      "121 624 11 0.5 1.0\n",
      "121 624 12 0.5 1.0\n",
      "121 624 13 0.5 1.0\n",
      "121 624 14 0.5 1.0\n",
      "121 624 15 0.5 1.0\n",
      "121 624 16 0.5 1.0\n",
      "121 624 17 0.5 1.0\n",
      "121 624 18 0.5 1.0\n",
      "121 624 19 0.5 1.0\n",
      "121 625 1 0.5 1.0\n",
      "121 625 2 0.5 1.0\n",
      "121 625 3 0.5 1.0\n",
      "121 625 4 0.5 1.0\n",
      "121 625 5 0.5 1.0\n",
      "121 625 6 0.5 1.0\n",
      "121 625 7 0.5 1.0\n",
      "121 625 8 0.5 1.0\n",
      "121 625 9 0.5 1.0\n",
      "121 625 10 0.5 1.0\n",
      "121 625 11 0.5 1.0\n",
      "121 625 12 0.5 1.0\n",
      "121 625 13 0.5 1.0\n",
      "121 625 14 0.5 1.0\n",
      "121 625 15 0.5 1.0\n",
      "121 625 16 0.5 1.0\n",
      "121 626 1 0.5 1.0\n",
      "121 626 2 0.5 1.0\n",
      "121 626 3 0.5 1.0\n",
      "121 626 4 0.5 1.0\n",
      "121 626 5 0.5 1.0\n",
      "121 626 6 0.5 1.0\n",
      "121 626 7 0.5 1.0\n",
      "121 626 8 0.5 1.0\n",
      "121 626 9 0.5 1.0\n",
      "121 626 10 0.5 1.0\n",
      "121 626 11 0.5 1.0\n",
      "121 626 12 0.5 1.0\n",
      "121 626 13 0.5 1.0\n",
      "121 626 14 0.5 1.0\n",
      "121 626 15 0.5 1.0\n",
      "121 626 16 0.5 1.0\n",
      "121 626 17 0.5 1.0\n",
      "121 626 18 0.5 1.0\n",
      "121 627 1 0.5 1.0\n",
      "121 627 2 0.5 1.0\n",
      "121 627 3 0.5 1.0\n",
      "121 627 4 0.5 1.0\n",
      "121 627 5 0.5 1.0\n",
      "121 627 6 0.5 1.0\n",
      "121 627 7 0.5 1.0\n",
      "121 627 8 0.5 1.0\n",
      "121 627 9 0.5 1.0\n",
      "121 627 10 0.5 1.0\n",
      "121 627 11 0.5 1.0\n",
      "121 627 12 0.5 1.0\n",
      "121 627 13 0.5 1.0\n",
      "121 627 14 0.5 1.0\n",
      "121 627 15 0.5 1.0\n",
      "121 627 16 0.5 1.0\n",
      "121 627 17 0.5 1.0\n",
      "121 627 18 0.5 1.0\n",
      "121 628 1 0.5 1.0\n",
      "121 628 2 0.5 1.0\n",
      "121 628 3 0.5 1.0\n",
      "121 628 4 0.5 1.0\n",
      "121 628 5 0.5 1.0\n",
      "121 628 6 0.5 1.0\n",
      "121 628 7 0.5 1.0\n",
      "121 628 8 0.5 1.0\n",
      "121 628 9 0.5 1.0\n",
      "121 628 10 0.5 1.0\n",
      "121 628 11 0.5 1.0\n",
      "121 628 12 0.5 1.0\n",
      "121 628 13 0.5 1.0\n",
      "121 628 14 0.5 1.0\n",
      "121 629 1 0.5 1.0\n",
      "121 629 2 0.5 1.0\n",
      "121 629 3 0.5 1.0\n",
      "121 629 4 0.5 1.0\n",
      "121 629 5 0.5 1.0\n",
      "121 629 6 0.5 1.0\n",
      "121 629 7 0.5 1.0\n",
      "121 629 8 0.5 1.0\n",
      "121 629 9 0.5 1.0\n",
      "121 629 10 0.5 1.0\n",
      "121 629 11 0.5 1.0\n",
      "121 629 12 0.5 1.0\n",
      "121 629 13 0.5 1.0\n",
      "121 629 14 0.5 1.0\n",
      "122 603 1 0.5 1.0\n",
      "122 603 2 0.5 1.0\n",
      "122 603 3 0.5 1.0\n",
      "122 603 4 0.5 1.0\n",
      "122 603 5 0.5 1.0\n",
      "122 604 1 0.5 1.0\n",
      "122 604 2 0.5 1.0\n",
      "122 604 3 0.5 1.0\n",
      "122 604 4 0.5 1.0\n",
      "122 604 5 0.5 1.0\n",
      "122 605 1 0.5 1.0\n",
      "122 605 2 0.5 1.0\n",
      "122 605 3 0.5 1.0\n",
      "122 605 4 0.5 1.0\n",
      "122 605 5 0.5 1.0\n",
      "122 606 1 0.5 1.0\n",
      "122 606 2 0.5 1.0\n",
      "122 606 3 0.5 1.0\n",
      "122 606 4 0.5 1.0\n",
      "122 606 5 0.5 1.0\n",
      "122 607 1 0.5 1.0\n",
      "122 607 2 0.5 1.0\n",
      "122 607 3 0.5 1.0\n",
      "122 612 1 0.5 1.0\n",
      "122 612 2 0.5 1.0\n",
      "122 612 3 0.5 1.0\n",
      "122 612 4 0.5 1.0\n",
      "122 612 5 0.5 1.0\n",
      "122 612 6 0.5 1.0\n",
      "122 612 7 0.5 1.0\n",
      "122 613 1 0.5 1.0\n",
      "122 613 2 0.5 1.0\n",
      "122 613 3 0.5 1.0\n",
      "122 613 4 0.5 1.0\n",
      "122 613 5 0.5 1.0\n",
      "122 613 6 0.5 1.0\n",
      "122 613 7 0.5 1.0\n",
      "122 613 8 0.5 1.0\n",
      "122 613 9 0.5 1.0\n",
      "122 613 10 0.5 1.0\n",
      "122 613 11 0.5 1.0\n",
      "122 613 12 0.5 1.0\n",
      "122 614 1 0.5 1.0\n",
      "122 614 2 0.5 1.0\n",
      "122 614 3 0.5 1.0\n",
      "122 614 4 0.5 1.0\n",
      "122 614 5 0.5 1.0\n",
      "122 614 6 0.5 1.0\n",
      "122 614 7 0.5 1.0\n",
      "122 614 8 0.5 1.0\n",
      "122 614 9 0.5 1.0\n",
      "122 614 10 0.5 1.0\n",
      "122 614 11 0.5 1.0\n",
      "122 614 12 0.5 1.0\n",
      "122 614 13 0.5 1.0\n",
      "122 614 14 0.5 1.0\n",
      "122 614 15 0.5 1.0\n",
      "122 614 16 0.5 1.0\n",
      "122 614 17 0.5 1.0\n",
      "122 614 18 0.5 1.0\n",
      "122 614 19 0.5 1.0\n",
      "122 614 20 0.5 1.0\n",
      "122 615 1 0.5 1.0\n",
      "122 615 2 0.5 1.0\n",
      "122 615 3 0.5 1.0\n",
      "122 615 4 0.5 1.0\n",
      "122 615 5 0.5 1.0\n",
      "122 615 6 0.5 1.0\n",
      "122 615 7 0.5 1.0\n",
      "122 615 8 0.5 1.0\n",
      "122 615 9 0.5 1.0\n",
      "122 615 10 0.5 1.0\n",
      "122 615 11 0.5 1.0\n",
      "122 615 12 0.5 1.0\n",
      "122 615 13 0.5 1.0\n",
      "122 615 14 0.5 1.0\n",
      "122 615 15 0.5 1.0\n",
      "122 615 16 0.5 1.0\n",
      "122 615 17 0.5 1.0\n",
      "122 615 18 0.5 1.0\n",
      "122 615 19 0.5 1.0\n",
      "122 615 20 0.5 1.0\n",
      "122 616 1 0.5 1.0\n",
      "122 616 2 0.5 1.0\n",
      "122 616 3 0.5 1.0\n",
      "122 616 4 0.5 1.0\n",
      "122 616 5 0.5 1.0\n",
      "122 616 6 0.5 1.0\n",
      "122 616 7 0.5 1.0\n",
      "122 616 8 0.5 1.0\n",
      "122 616 9 0.5 1.0\n",
      "122 616 10 0.5 1.0\n",
      "122 616 11 0.5 1.0\n",
      "122 616 12 0.5 1.0\n",
      "122 616 13 0.5 1.0\n",
      "122 616 14 0.5 1.0\n",
      "122 616 15 0.5 1.0\n",
      "122 616 16 0.5 1.0\n",
      "122 617 1 0.5 1.0\n",
      "122 617 2 0.5 1.0\n",
      "122 617 3 0.5 1.0\n",
      "122 617 4 0.5 1.0\n",
      "122 617 5 0.5 1.0\n",
      "122 617 6 0.5 1.0\n",
      "122 617 7 0.5 1.0\n",
      "122 617 8 0.5 1.0\n",
      "122 617 9 0.5 1.0\n",
      "122 617 10 0.5 1.0\n",
      "122 617 11 0.5 1.0\n",
      "122 617 12 0.5 1.0\n",
      "122 617 13 0.5 1.0\n",
      "122 617 14 0.5 1.0\n",
      "122 617 15 0.5 1.0\n",
      "122 617 16 0.5 1.0\n",
      "122 617 17 0.5 1.0\n",
      "122 618 1 0.5 1.0\n",
      "122 618 2 0.5 1.0\n",
      "122 618 3 0.5 1.0\n",
      "122 618 4 0.5 1.0\n",
      "122 618 5 0.5 1.0\n",
      "122 618 6 0.5 1.0\n",
      "122 618 7 0.5 1.0\n",
      "122 618 8 0.5 1.0\n",
      "122 618 9 0.5 1.0\n",
      "122 618 10 0.5 1.0\n",
      "122 618 11 0.5 1.0\n",
      "122 618 12 0.5 1.0\n",
      "122 618 13 0.5 1.0\n",
      "122 618 14 0.5 1.0\n",
      "122 618 15 0.5 1.0\n",
      "122 618 16 0.5 1.0\n",
      "122 618 17 0.5 1.0\n",
      "122 618 18 0.5 1.0\n",
      "122 618 19 0.5 1.0\n",
      "122 618 20 0.5 1.0\n",
      "122 619 1 0.5 1.0\n",
      "122 619 2 0.5 1.0\n",
      "122 619 3 0.5 1.0\n",
      "122 619 4 0.5 1.0\n",
      "122 619 5 0.5 1.0\n",
      "122 619 6 0.5 1.0\n",
      "122 619 7 0.5 1.0\n",
      "122 619 8 0.5 1.0\n",
      "122 619 9 0.5 1.0\n",
      "122 619 10 0.5 1.0\n",
      "122 619 11 0.5 1.0\n",
      "122 619 12 0.5 1.0\n",
      "122 619 13 0.5 1.0\n",
      "122 619 14 0.5 1.0\n",
      "122 619 15 0.5 1.0\n",
      "122 619 16 0.5 1.0\n",
      "122 625 1 0.5 1.0\n",
      "122 625 2 0.5 1.0\n",
      "122 625 3 0.5 1.0\n",
      "122 625 4 0.5 1.0\n",
      "122 625 5 0.5 1.0\n",
      "122 625 6 0.5 1.0\n",
      "122 625 7 0.5 1.0\n",
      "122 625 8 0.5 1.0\n",
      "122 625 9 0.5 1.0\n",
      "122 625 10 0.5 1.0\n",
      "122 626 1 0.5 1.0\n",
      "122 626 2 0.5 1.0\n",
      "122 626 3 0.5 1.0\n",
      "122 626 4 0.5 1.0\n",
      "122 626 5 0.5 1.0\n",
      "122 626 6 0.5 1.0\n",
      "122 626 7 0.5 1.0\n",
      "122 626 8 0.5 1.0\n",
      "122 626 9 0.5 1.0\n",
      "122 626 10 0.5 1.0\n",
      "122 627 1 0.5 1.0\n",
      "122 627 2 0.5 1.0\n",
      "122 627 3 0.5 1.0\n",
      "122 627 4 0.5 1.0\n",
      "122 627 5 0.5 1.0\n",
      "122 627 6 0.5 1.0\n",
      "122 627 7 0.5 1.0\n",
      "122 627 8 0.5 1.0\n",
      "122 627 9 0.5 1.0\n",
      "122 627 10 0.5 1.0\n",
      "122 627 11 0.5 1.0\n",
      "122 627 12 0.5 1.0\n",
      "122 627 13 0.5 1.0\n",
      "122 627 14 0.5 1.0\n",
      "122 627 15 0.5 1.0\n",
      "122 627 16 0.5 1.0\n",
      "122 628 1 0.5 1.0\n",
      "122 628 2 0.5 1.0\n",
      "122 628 3 0.5 1.0\n",
      "122 628 4 0.5 1.0\n",
      "122 628 5 0.5 1.0\n",
      "122 628 6 0.5 1.0\n",
      "122 628 7 0.5 1.0\n",
      "122 628 8 0.5 1.0\n",
      "122 628 9 0.5 1.0\n",
      "122 628 10 0.5 1.0\n",
      "122 628 11 0.5 1.0\n",
      "122 628 12 0.5 1.0\n",
      "122 628 13 0.5 1.0\n",
      "122 628 14 0.5 1.0\n",
      "122 628 15 0.5 1.0\n",
      "122 628 16 0.5 1.0\n",
      "122 628 17 0.5 1.0\n",
      "122 628 18 0.5 1.0\n",
      "122 628 19 0.5 1.0\n",
      "122 628 20 0.5 1.0\n",
      "122 629 1 0.5 1.0\n",
      "122 629 2 0.5 1.0\n",
      "122 629 3 0.5 1.0\n",
      "122 629 4 0.5 1.0\n",
      "122 629 5 0.5 1.0\n",
      "122 629 6 0.5 1.0\n",
      "122 629 7 0.5 1.0\n",
      "122 629 8 0.5 1.0\n",
      "122 629 9 0.5 1.0\n",
      "122 629 10 0.5 1.0\n",
      "122 629 11 0.5 1.0\n",
      "122 629 12 0.5 1.0\n",
      "122 629 13 0.5 1.0\n",
      "122 629 14 0.5 1.0\n",
      "122 629 15 0.5 1.0\n",
      "122 629 16 0.5 1.0\n",
      "122 629 17 0.5 1.0\n",
      "122 629 18 0.5 1.0\n",
      "122 629 19 0.5 1.0\n",
      "123 602 1 0.5 1.0\n",
      "123 602 2 0.5 1.0\n",
      "123 602 3 0.5 1.0\n",
      "123 602 4 0.5 1.0\n",
      "123 602 5 0.5 1.0\n",
      "123 602 6 0.5 1.0\n",
      "123 602 7 0.5 1.0\n",
      "123 603 1 0.5 1.0\n",
      "123 603 2 0.5 1.0\n",
      "123 603 3 0.5 1.0\n",
      "123 603 4 0.5 1.0\n",
      "123 603 5 0.5 1.0\n",
      "123 603 6 0.5 1.0\n",
      "123 603 7 0.5 1.0\n",
      "123 603 8 0.5 1.0\n",
      "123 603 9 0.5 1.0\n",
      "123 604 1 0.5 1.0\n",
      "123 604 2 0.5 1.0\n",
      "123 604 3 0.5 1.0\n",
      "123 604 4 0.5 1.0\n",
      "123 604 5 0.5 1.0\n",
      "123 604 6 0.5 1.0\n",
      "123 604 7 0.5 1.0\n",
      "123 604 8 0.5 1.0\n",
      "123 604 9 0.5 1.0\n",
      "123 604 10 0.5 1.0\n",
      "123 604 11 0.5 1.0\n",
      "123 605 1 0.5 1.0\n",
      "123 605 2 0.5 1.0\n",
      "123 605 3 0.5 1.0\n",
      "123 605 4 0.5 1.0\n",
      "123 605 5 0.5 1.0\n",
      "123 605 6 0.5 1.0\n",
      "123 605 7 0.5 1.0\n",
      "123 605 8 0.5 1.0\n",
      "123 605 9 0.5 1.0\n",
      "123 605 10 0.5 1.0\n",
      "123 606 1 0.5 1.0\n",
      "123 606 2 0.5 1.0\n",
      "123 606 3 0.5 1.0\n",
      "123 606 4 0.5 1.0\n",
      "123 606 5 0.5 1.0\n",
      "123 606 6 0.5 1.0\n",
      "123 606 7 0.5 1.0\n",
      "123 606 8 0.5 1.0\n",
      "123 606 9 0.5 1.0\n",
      "123 606 10 0.5 1.0\n",
      "123 607 1 0.5 1.0\n",
      "123 607 2 0.5 1.0\n",
      "123 607 3 0.5 1.0\n",
      "123 607 4 0.5 1.0\n",
      "123 607 5 0.5 1.0\n",
      "123 607 6 0.5 1.0\n",
      "123 607 7 0.5 1.0\n",
      "123 607 8 0.5 1.0\n",
      "123 608 1 0.5 1.0\n",
      "123 608 2 0.5 1.0\n",
      "123 608 3 0.5 1.0\n",
      "123 608 4 0.5 1.0\n",
      "123 608 5 0.5 1.0\n",
      "123 608 6 0.5 1.0\n",
      "123 608 7 0.5 1.0\n",
      "123 611 1 0.5 1.0\n",
      "123 611 2 0.5 1.0\n",
      "123 611 3 0.5 1.0\n",
      "123 611 4 0.5 1.0\n",
      "123 611 5 0.5 1.0\n",
      "123 611 6 0.5 1.0\n",
      "123 611 7 0.5 1.0\n",
      "123 611 8 0.5 1.0\n",
      "123 612 1 0.5 1.0\n",
      "123 612 2 0.5 1.0\n",
      "123 612 3 0.5 1.0\n",
      "123 612 4 0.5 1.0\n",
      "123 612 5 0.5 1.0\n",
      "123 612 6 0.5 1.0\n",
      "123 612 7 0.5 1.0\n",
      "123 612 8 0.5 1.0\n",
      "123 612 9 0.5 1.0\n",
      "123 612 10 0.5 1.0\n",
      "123 612 11 0.5 1.0\n",
      "123 612 12 0.5 1.0\n",
      "123 612 13 0.5 1.0\n",
      "123 613 1 0.5 1.0\n",
      "123 613 2 0.5 1.0\n",
      "123 613 3 0.5 1.0\n",
      "123 613 4 0.5 1.0\n",
      "123 613 5 0.5 1.0\n",
      "123 613 6 0.5 1.0\n",
      "123 613 7 0.5 1.0\n",
      "123 613 8 0.5 1.0\n",
      "123 613 9 0.5 1.0\n",
      "123 613 10 0.5 1.0\n",
      "123 613 11 0.5 1.0\n",
      "123 613 12 0.5 1.0\n",
      "123 613 13 0.5 1.0\n",
      "123 613 14 0.5 1.0\n",
      "123 613 15 0.5 1.0\n",
      "123 613 16 0.5 1.0\n",
      "123 613 17 0.5 1.0\n",
      "123 613 18 0.5 1.0\n",
      "123 613 19 0.5 1.0\n",
      "123 613 20 0.5 1.0\n",
      "123 614 1 0.5 1.0\n",
      "123 614 2 0.5 1.0\n",
      "123 614 3 0.5 1.0\n",
      "123 614 4 0.5 1.0\n",
      "123 614 5 0.5 1.0\n",
      "123 614 6 0.5 1.0\n",
      "123 614 7 0.5 1.0\n",
      "123 614 8 0.5 1.0\n",
      "123 614 9 0.5 1.0\n",
      "123 614 10 0.5 1.0\n",
      "123 614 11 0.5 1.0\n",
      "123 614 12 0.5 1.0\n",
      "123 614 13 0.5 1.0\n",
      "123 614 14 0.5 1.0\n",
      "123 614 15 0.5 1.0\n",
      "123 614 16 0.5 1.0\n",
      "123 614 17 0.5 1.0\n",
      "123 614 18 0.5 1.0\n",
      "123 614 19 0.5 1.0\n",
      "123 614 20 0.5 1.0\n",
      "123 614 21 0.5 1.0\n",
      "123 615 1 0.5 1.0\n",
      "123 615 2 0.5 1.0\n",
      "123 615 3 0.5 1.0\n",
      "123 615 4 0.5 1.0\n",
      "123 615 5 0.5 1.0\n",
      "123 615 6 0.5 1.0\n",
      "123 615 7 0.5 1.0\n",
      "123 615 8 0.5 1.0\n",
      "123 615 9 0.5 1.0\n",
      "123 615 10 0.5 1.0\n",
      "123 615 11 0.5 1.0\n",
      "123 615 12 0.5 1.0\n",
      "123 615 13 0.5 1.0\n",
      "123 615 14 0.5 1.0\n",
      "123 615 15 0.5 1.0\n",
      "123 615 16 0.5 1.0\n",
      "123 615 17 0.5 1.0\n",
      "123 615 18 0.5 1.0\n",
      "123 615 19 0.5 1.0\n",
      "123 616 1 0.5 1.0\n",
      "123 616 2 0.5 1.0\n",
      "123 616 3 0.5 1.0\n",
      "123 616 4 0.5 1.0\n",
      "123 616 5 0.5 1.0\n",
      "123 616 6 0.5 1.0\n",
      "123 616 7 0.5 1.0\n",
      "123 616 8 0.5 1.0\n",
      "123 616 9 0.5 1.0\n",
      "123 616 10 0.5 1.0\n",
      "123 616 11 0.5 1.0\n",
      "123 616 12 0.5 1.0\n",
      "123 616 13 0.5 1.0\n",
      "123 616 14 0.5 1.0\n",
      "123 616 15 0.5 1.0\n",
      "123 616 16 0.5 1.0\n",
      "123 616 17 0.5 1.0\n",
      "123 616 18 0.5 1.0\n",
      "123 616 19 0.5 1.0\n",
      "123 617 1 0.5 1.0\n",
      "123 617 2 0.5 1.0\n",
      "123 617 3 0.5 1.0\n",
      "123 617 4 0.5 1.0\n",
      "123 617 5 0.5 1.0\n",
      "123 617 6 0.5 1.0\n",
      "123 617 7 0.5 1.0\n",
      "123 617 8 0.5 1.0\n",
      "123 617 9 0.5 1.0\n",
      "123 617 10 0.5 1.0\n",
      "123 617 11 0.5 1.0\n",
      "123 617 12 0.5 1.0\n",
      "123 617 13 0.5 1.0\n",
      "123 626 1 0.5 1.0\n",
      "123 626 2 0.5 1.0\n",
      "123 626 3 0.5 1.0\n",
      "123 626 4 0.5 1.0\n",
      "123 626 5 0.5 1.0\n",
      "123 627 1 0.5 1.0\n",
      "123 627 2 0.5 1.0\n",
      "123 627 3 0.5 1.0\n",
      "123 627 4 0.5 1.0\n",
      "123 627 5 0.5 1.0\n",
      "123 627 6 0.5 1.0\n",
      "123 627 7 0.5 1.0\n",
      "123 627 8 0.5 1.0\n",
      "123 627 9 0.5 1.0\n",
      "123 628 1 0.5 1.0\n",
      "123 628 2 0.5 1.0\n",
      "123 628 3 0.5 1.0\n",
      "123 628 4 0.5 1.0\n",
      "123 628 5 0.5 1.0\n",
      "123 628 6 0.5 1.0\n",
      "123 628 7 0.5 1.0\n",
      "123 628 8 0.5 1.0\n",
      "123 628 9 0.5 1.0\n",
      "123 628 10 0.5 1.0\n",
      "123 628 11 0.5 1.0\n",
      "123 628 12 0.5 1.0\n",
      "123 628 13 0.5 1.0\n",
      "123 628 14 0.5 1.0\n",
      "123 628 15 0.5 1.0\n",
      "123 628 16 0.5 1.0\n",
      "123 629 1 0.5 1.0\n",
      "123 629 2 0.5 1.0\n",
      "123 629 3 0.5 1.0\n",
      "123 629 4 0.5 1.0\n",
      "123 629 5 0.5 1.0\n",
      "123 629 6 0.5 1.0\n",
      "123 629 7 0.5 1.0\n",
      "123 629 8 0.5 1.0\n",
      "123 629 9 0.5 1.0\n",
      "123 629 10 0.5 1.0\n",
      "123 629 11 0.5 1.0\n",
      "123 629 12 0.5 1.0\n",
      "123 629 13 0.5 1.0\n",
      "123 629 14 0.5 1.0\n",
      "123 629 15 0.5 1.0\n",
      "123 629 16 0.5 1.0\n",
      "123 629 17 0.5 1.0\n",
      "123 629 18 0.5 1.0\n",
      "123 629 19 0.5 1.0\n",
      "123 629 20 0.5 1.0\n",
      "124 601 1 0.5 1.0\n",
      "124 601 2 0.5 1.0\n",
      "124 601 3 0.5 1.0\n",
      "124 601 4 0.5 1.0\n",
      "124 601 5 0.5 1.0\n",
      "124 601 6 0.5 1.0\n",
      "124 601 7 0.5 1.0\n",
      "124 602 1 0.5 1.0\n",
      "124 602 2 0.5 1.0\n",
      "124 602 3 0.5 1.0\n",
      "124 602 4 0.5 1.0\n",
      "124 602 5 0.5 1.0\n",
      "124 602 6 0.5 1.0\n",
      "124 602 7 0.5 1.0\n",
      "124 602 8 0.5 1.0\n",
      "124 602 9 0.5 1.0\n",
      "124 602 10 0.5 1.0\n",
      "124 602 11 0.5 1.0\n",
      "124 602 12 0.5 1.0\n",
      "124 602 13 0.5 1.0\n",
      "124 603 1 0.5 1.0\n",
      "124 603 2 0.5 1.0\n",
      "124 603 3 0.5 1.0\n",
      "124 603 4 0.5 1.0\n",
      "124 603 5 0.5 1.0\n",
      "124 603 6 0.5 1.0\n",
      "124 603 7 0.5 1.0\n",
      "124 603 8 0.5 1.0\n",
      "124 603 9 0.5 1.0\n",
      "124 603 10 0.5 1.0\n",
      "124 603 11 0.5 1.0\n",
      "124 603 12 0.5 1.0\n",
      "124 603 13 0.5 1.0\n",
      "124 603 14 0.5 1.0\n",
      "124 603 15 0.5 1.0\n",
      "124 603 16 0.5 1.0\n",
      "124 603 17 0.5 1.0\n",
      "124 603 18 0.5 1.0\n",
      "124 603 19 0.5 1.0\n",
      "124 604 1 0.5 1.0\n",
      "124 604 2 0.5 1.0\n",
      "124 604 3 0.5 1.0\n",
      "124 604 4 0.5 1.0\n",
      "124 604 5 0.5 1.0\n",
      "124 604 6 0.5 1.0\n",
      "124 604 7 0.5 1.0\n",
      "124 604 8 0.5 1.0\n",
      "124 604 9 0.5 1.0\n",
      "124 604 10 0.5 1.0\n",
      "124 604 11 0.5 1.0\n",
      "124 604 12 0.5 1.0\n",
      "124 604 13 0.5 1.0\n",
      "124 604 14 0.5 1.0\n",
      "124 604 15 0.5 1.0\n",
      "124 604 16 0.5 1.0\n",
      "124 604 17 0.5 1.0\n",
      "124 604 18 0.5 1.0\n",
      "124 604 19 0.5 1.0\n",
      "124 604 20 0.5 1.0\n",
      "124 605 1 0.5 1.0\n",
      "124 605 2 0.5 1.0\n",
      "124 605 3 0.5 1.0\n",
      "124 605 4 0.5 1.0\n",
      "124 605 5 0.5 1.0\n",
      "124 605 6 0.5 1.0\n",
      "124 605 7 0.5 1.0\n",
      "124 605 8 0.5 1.0\n",
      "124 605 9 0.5 1.0\n",
      "124 605 10 0.5 1.0\n",
      "124 605 11 0.5 1.0\n",
      "124 605 12 0.5 1.0\n",
      "124 605 13 0.5 1.0\n",
      "124 605 14 0.5 1.0\n",
      "124 605 15 0.5 1.0\n",
      "124 605 16 0.5 1.0\n",
      "124 605 17 0.5 1.0\n",
      "124 605 18 0.5 1.0\n",
      "124 605 19 0.5 1.0\n",
      "124 606 1 0.5 1.0\n",
      "124 606 2 0.5 1.0\n",
      "124 606 3 0.5 1.0\n",
      "124 606 4 0.5 1.0\n",
      "124 606 5 0.5 1.0\n",
      "124 606 6 0.5 1.0\n",
      "124 606 7 0.5 1.0\n",
      "124 606 8 0.5 1.0\n",
      "124 606 9 0.5 1.0\n",
      "124 606 10 0.5 1.0\n",
      "124 606 11 0.5 1.0\n",
      "124 606 12 0.5 1.0\n",
      "124 606 13 0.5 1.0\n",
      "124 606 14 0.5 1.0\n",
      "124 606 15 0.5 1.0\n",
      "124 606 16 0.5 1.0\n",
      "124 606 17 0.5 1.0\n",
      "124 606 18 0.5 1.0\n",
      "124 607 1 0.5 1.0\n",
      "124 607 2 0.5 1.0\n",
      "124 607 3 0.5 1.0\n",
      "124 607 4 0.5 1.0\n",
      "124 607 5 0.5 1.0\n",
      "124 607 6 0.5 1.0\n",
      "124 607 7 0.5 1.0\n",
      "124 607 8 0.5 1.0\n",
      "124 607 9 0.5 1.0\n",
      "124 607 10 0.5 1.0\n",
      "124 607 11 0.5 1.0\n",
      "124 607 12 0.5 1.0\n",
      "124 607 13 0.5 1.0\n",
      "124 607 14 0.5 1.0\n",
      "124 607 15 0.5 1.0\n",
      "124 607 16 0.5 1.0\n",
      "124 608 1 0.5 1.0\n",
      "124 608 2 0.5 1.0\n",
      "124 608 3 0.5 1.0\n",
      "124 608 4 0.5 1.0\n",
      "124 608 5 0.5 1.0\n",
      "124 608 6 0.5 1.0\n",
      "124 608 7 0.5 1.0\n",
      "124 608 8 0.5 1.0\n",
      "124 608 9 0.5 1.0\n",
      "124 608 10 0.5 1.0\n",
      "124 608 11 0.5 1.0\n",
      "124 608 12 0.5 1.0\n",
      "124 608 13 0.5 1.0\n",
      "124 609 1 0.5 1.0\n",
      "124 609 2 0.5 1.0\n",
      "124 609 3 0.5 1.0\n",
      "124 609 4 0.5 1.0\n",
      "124 609 5 0.5 1.0\n",
      "124 609 6 0.5 1.0\n",
      "124 609 7 0.5 1.0\n",
      "124 609 8 0.5 1.0\n",
      "124 609 9 0.5 1.0\n",
      "124 609 10 0.5 1.0\n",
      "124 609 11 0.5 1.0\n",
      "124 609 12 0.5 1.0\n",
      "124 609 13 0.5 1.0\n",
      "124 609 14 0.5 1.0\n",
      "124 609 15 0.5 1.0\n",
      "124 609 16 0.5 1.0\n",
      "124 609 17 0.5 1.0\n",
      "124 610 1 0.5 1.0\n",
      "124 610 2 0.5 1.0\n",
      "124 610 3 0.5 1.0\n",
      "124 610 4 0.5 1.0\n",
      "124 610 5 0.5 1.0\n",
      "124 610 6 0.5 1.0\n",
      "124 610 7 0.5 1.0\n",
      "124 610 8 0.5 1.0\n",
      "124 610 9 0.5 1.0\n",
      "124 610 10 0.5 1.0\n",
      "124 610 11 0.5 1.0\n",
      "124 610 12 0.5 1.0\n",
      "124 610 13 0.5 1.0\n",
      "124 610 14 0.5 1.0\n",
      "124 610 15 0.5 1.0\n",
      "124 610 16 0.5 1.0\n",
      "124 610 17 0.5 1.0\n",
      "124 611 1 0.5 1.0\n",
      "124 611 2 0.5 1.0\n",
      "124 611 3 0.5 1.0\n",
      "124 611 4 0.5 1.0\n",
      "124 611 5 0.5 1.0\n",
      "124 611 6 0.5 1.0\n",
      "124 611 7 0.5 1.0\n",
      "124 611 8 0.5 1.0\n",
      "124 611 9 0.5 1.0\n",
      "124 611 10 0.5 1.0\n",
      "124 611 11 0.5 1.0\n",
      "124 611 12 0.5 1.0\n",
      "124 611 13 0.5 1.0\n",
      "124 611 14 0.5 1.0\n",
      "124 611 15 0.5 1.0\n",
      "124 611 16 0.5 1.0\n",
      "124 611 17 0.5 1.0\n",
      "124 611 18 0.5 1.0\n",
      "124 612 1 0.5 1.0\n",
      "124 612 2 0.5 1.0\n",
      "124 612 3 0.5 1.0\n",
      "124 612 4 0.5 1.0\n",
      "124 612 5 0.5 1.0\n",
      "124 612 6 0.5 1.0\n",
      "124 612 7 0.5 1.0\n",
      "124 612 8 0.5 1.0\n",
      "124 612 9 0.5 1.0\n",
      "124 612 10 0.5 1.0\n",
      "124 612 11 0.5 1.0\n",
      "124 612 12 0.5 1.0\n",
      "124 612 13 0.5 1.0\n",
      "124 612 14 0.5 1.0\n",
      "124 612 15 0.5 1.0\n",
      "124 612 16 0.5 1.0\n",
      "124 612 17 0.5 1.0\n",
      "124 612 18 0.5 1.0\n",
      "124 612 19 0.5 1.0\n",
      "124 612 20 0.5 1.0\n",
      "124 613 1 0.5 1.0\n",
      "124 613 2 0.5 1.0\n",
      "124 613 3 0.5 1.0\n",
      "124 613 4 0.5 1.0\n",
      "124 613 5 0.5 1.0\n",
      "124 613 6 0.5 1.0\n",
      "124 613 7 0.5 1.0\n",
      "124 613 8 0.5 1.0\n",
      "124 613 9 0.5 1.0\n",
      "124 613 10 0.5 1.0\n",
      "124 613 11 0.5 1.0\n",
      "124 613 12 0.5 1.0\n",
      "124 613 13 0.5 1.0\n",
      "124 613 14 0.5 1.0\n",
      "124 613 15 0.5 1.0\n",
      "124 613 16 0.5 1.0\n",
      "124 613 17 0.5 1.0\n",
      "124 613 18 0.5 1.0\n",
      "124 613 19 0.5 1.0\n",
      "124 613 20 0.5 1.0\n",
      "124 613 21 0.5 1.0\n",
      "124 614 1 0.5 1.0\n",
      "124 614 2 0.5 1.0\n",
      "124 614 3 0.5 1.0\n",
      "124 614 4 0.5 1.0\n",
      "124 614 5 0.5 1.0\n",
      "124 614 6 0.5 1.0\n",
      "124 614 7 0.5 1.0\n",
      "124 614 8 0.5 1.0\n",
      "124 614 9 0.5 1.0\n",
      "124 614 10 0.5 1.0\n",
      "124 614 11 0.5 1.0\n",
      "124 614 12 0.5 1.0\n",
      "124 614 13 0.5 1.0\n",
      "124 614 14 0.5 1.0\n",
      "124 614 15 0.5 1.0\n",
      "124 614 16 0.5 1.0\n",
      "124 614 17 0.5 1.0\n",
      "124 614 18 0.5 1.0\n",
      "124 615 1 0.5 1.0\n",
      "124 615 2 0.5 1.0\n",
      "124 615 3 0.5 1.0\n",
      "124 615 4 0.5 1.0\n",
      "124 615 5 0.5 1.0\n",
      "124 615 6 0.5 1.0\n",
      "124 615 7 0.5 1.0\n",
      "124 615 8 0.5 1.0\n",
      "124 615 9 0.5 1.0\n",
      "124 615 10 0.5 1.0\n",
      "124 615 11 0.5 1.0\n",
      "124 628 1 0.5 1.0\n",
      "124 628 2 0.5 1.0\n",
      "124 628 3 0.5 1.0\n",
      "124 628 4 0.5 1.0\n",
      "124 628 5 0.5 1.0\n",
      "124 628 6 0.5 1.0\n",
      "124 628 7 0.5 1.0\n",
      "124 629 1 0.5 1.0\n",
      "124 629 2 0.5 1.0\n",
      "124 629 3 0.5 1.0\n",
      "124 629 4 0.5 1.0\n",
      "124 629 5 0.5 1.0\n",
      "124 629 6 0.5 1.0\n",
      "124 629 7 0.5 1.0\n",
      "124 629 8 0.5 1.0\n",
      "124 629 9 0.5 1.0\n",
      "124 629 10 0.5 1.0\n",
      "124 629 11 0.5 1.0\n",
      "124 629 12 0.5 1.0\n",
      "124 629 13 0.5 1.0\n",
      "124 629 14 0.5 1.0\n",
      "124 629 15 0.5 1.0\n",
      "125 599 1 0.5 1.0\n",
      "125 599 2 0.5 1.0\n",
      "125 599 3 0.5 1.0\n",
      "125 599 4 0.5 1.0\n",
      "125 599 5 0.5 1.0\n",
      "125 599 6 0.5 1.0\n",
      "125 600 1 0.5 1.0\n",
      "125 600 2 0.5 1.0\n",
      "125 600 3 0.5 1.0\n",
      "125 600 4 0.5 1.0\n",
      "125 600 5 0.5 1.0\n",
      "125 600 6 0.5 1.0\n",
      "125 600 7 0.5 1.0\n",
      "125 600 8 0.5 1.0\n",
      "125 600 9 0.5 1.0\n",
      "125 600 10 0.5 1.0\n",
      "125 600 11 0.5 1.0\n",
      "125 601 1 0.5 1.0\n",
      "125 601 2 0.5 1.0\n",
      "125 601 3 0.5 1.0\n",
      "125 601 4 0.5 1.0\n",
      "125 601 5 0.5 1.0\n",
      "125 601 6 0.5 1.0\n",
      "125 601 7 0.5 1.0\n",
      "125 601 8 0.5 1.0\n",
      "125 601 9 0.5 1.0\n",
      "125 601 10 0.5 1.0\n",
      "125 601 11 0.5 1.0\n",
      "125 601 12 0.5 1.0\n",
      "125 601 13 0.5 1.0\n",
      "125 601 14 0.5 1.0\n",
      "125 601 15 0.5 1.0\n",
      "125 601 16 0.5 1.0\n",
      "125 601 17 0.5 1.0\n",
      "125 602 1 0.5 1.0\n",
      "125 602 2 0.5 1.0\n",
      "125 602 3 0.5 1.0\n",
      "125 602 4 0.5 1.0\n",
      "125 602 5 0.5 1.0\n",
      "125 602 6 0.5 1.0\n",
      "125 602 7 0.5 1.0\n",
      "125 602 8 0.5 1.0\n",
      "125 602 9 0.5 1.0\n",
      "125 602 10 0.5 1.0\n",
      "125 602 11 0.5 1.0\n",
      "125 602 12 0.5 1.0\n",
      "125 602 13 0.5 1.0\n",
      "125 602 14 0.5 1.0\n",
      "125 602 15 0.5 1.0\n",
      "125 602 16 0.5 1.0\n",
      "125 602 17 0.5 1.0\n",
      "125 602 18 0.5 1.0\n",
      "125 602 19 0.5 1.0\n",
      "125 602 20 0.5 1.0\n",
      "125 602 21 0.5 1.0\n",
      "125 603 1 0.5 1.0\n",
      "125 603 2 0.5 1.0\n",
      "125 603 3 0.5 1.0\n",
      "125 603 4 0.5 1.0\n",
      "125 603 5 0.5 1.0\n",
      "125 603 6 0.5 1.0\n",
      "125 603 7 0.5 1.0\n",
      "125 603 8 0.5 1.0\n",
      "125 603 9 0.5 1.0\n",
      "125 603 10 0.5 1.0\n",
      "125 603 11 0.5 1.0\n",
      "125 603 12 0.5 1.0\n",
      "125 603 13 0.5 1.0\n",
      "125 603 14 0.5 1.0\n",
      "125 603 15 0.5 1.0\n",
      "125 603 16 0.5 1.0\n",
      "125 603 17 0.5 1.0\n",
      "125 603 18 0.5 1.0\n",
      "125 603 19 0.5 1.0\n",
      "125 603 20 0.5 1.0\n",
      "125 603 21 0.5 1.0\n",
      "125 603 22 0.5 1.0\n",
      "125 604 1 0.5 1.0\n",
      "125 604 2 0.5 1.0\n",
      "125 604 3 0.5 1.0\n",
      "125 604 4 0.5 1.0\n",
      "125 604 5 0.5 1.0\n",
      "125 604 6 0.5 1.0\n",
      "125 604 7 0.5 1.0\n",
      "125 604 8 0.5 1.0\n",
      "125 604 9 0.5 1.0\n",
      "125 604 10 0.5 1.0\n",
      "125 604 11 0.5 1.0\n",
      "125 604 12 0.5 1.0\n",
      "125 604 13 0.5 1.0\n",
      "125 604 14 0.5 1.0\n",
      "125 604 15 0.5 1.0\n",
      "125 604 16 0.5 1.0\n",
      "125 604 17 0.5 1.0\n",
      "125 604 18 0.5 1.0\n",
      "125 604 19 0.5 1.0\n",
      "125 604 20 0.5 1.0\n",
      "125 604 21 0.5 1.0\n",
      "125 604 22 0.5 1.0\n",
      "125 605 1 0.5 1.0\n",
      "125 605 2 0.5 1.0\n",
      "125 605 3 0.5 1.0\n",
      "125 605 4 0.5 1.0\n",
      "125 605 5 0.5 1.0\n",
      "125 605 6 0.5 1.0\n",
      "125 605 7 0.5 1.0\n",
      "125 605 8 0.5 1.0\n",
      "125 605 9 0.5 1.0\n",
      "125 605 10 0.5 1.0\n",
      "125 605 11 0.5 1.0\n",
      "125 605 12 0.5 1.0\n",
      "125 605 13 0.5 1.0\n",
      "125 605 14 0.5 1.0\n",
      "125 605 15 0.5 1.0\n",
      "125 605 16 0.5 1.0\n",
      "125 605 17 0.5 1.0\n",
      "125 605 18 0.5 1.0\n",
      "125 605 19 0.5 1.0\n",
      "125 605 20 0.5 1.0\n",
      "125 605 21 0.5 1.0\n",
      "125 605 22 0.5 1.0\n",
      "125 606 1 0.5 1.0\n",
      "125 606 2 0.5 1.0\n",
      "125 606 3 0.5 1.0\n",
      "125 606 4 0.5 1.0\n",
      "125 606 5 0.5 1.0\n",
      "125 606 6 0.5 1.0\n",
      "125 606 7 0.5 1.0\n",
      "125 606 8 0.5 1.0\n",
      "125 606 9 0.5 1.0\n",
      "125 606 10 0.5 1.0\n",
      "125 606 11 0.5 1.0\n",
      "125 606 12 0.5 1.0\n",
      "125 606 13 0.5 1.0\n",
      "125 606 14 0.5 1.0\n",
      "125 606 15 0.5 1.0\n",
      "125 606 16 0.5 1.0\n",
      "125 606 17 0.5 1.0\n",
      "125 606 18 0.5 1.0\n",
      "125 606 19 0.5 1.0\n",
      "125 606 20 0.5 1.0\n",
      "125 606 21 0.5 1.0\n",
      "125 606 22 0.5 1.0\n",
      "125 607 1 0.5 1.0\n",
      "125 607 2 0.5 1.0\n",
      "125 607 3 0.5 1.0\n",
      "125 607 4 0.5 1.0\n",
      "125 607 5 0.5 1.0\n",
      "125 607 6 0.5 1.0\n",
      "125 607 7 0.5 1.0\n",
      "125 607 8 0.5 1.0\n",
      "125 607 9 0.5 1.0\n",
      "125 607 10 0.5 1.0\n",
      "125 607 11 0.5 1.0\n",
      "125 607 12 0.5 1.0\n",
      "125 607 13 0.5 1.0\n",
      "125 607 14 0.5 1.0\n",
      "125 607 15 0.5 1.0\n",
      "125 607 16 0.5 1.0\n",
      "125 607 17 0.5 1.0\n",
      "125 607 18 0.5 1.0\n",
      "125 607 19 0.5 1.0\n",
      "125 607 20 0.5 1.0\n",
      "125 607 21 0.5 1.0\n",
      "125 608 1 0.5 1.0\n",
      "125 608 2 0.5 1.0\n",
      "125 608 3 0.5 1.0\n",
      "125 608 4 0.5 1.0\n",
      "125 608 5 0.5 1.0\n",
      "125 608 6 0.5 1.0\n",
      "125 608 7 0.5 1.0\n",
      "125 608 8 0.5 1.0\n",
      "125 608 9 0.5 1.0\n",
      "125 608 10 0.5 1.0\n",
      "125 608 11 0.5 1.0\n",
      "125 608 12 0.5 1.0\n",
      "125 608 13 0.5 1.0\n",
      "125 608 14 0.5 1.0\n",
      "125 608 15 0.5 1.0\n",
      "125 608 16 0.5 1.0\n",
      "125 608 17 0.5 1.0\n",
      "125 608 18 0.5 1.0\n",
      "125 608 19 0.5 1.0\n",
      "125 608 20 0.5 1.0\n",
      "125 608 21 0.5 1.0\n",
      "125 609 1 0.5 1.0\n",
      "125 609 2 0.5 1.0\n",
      "125 609 3 0.5 1.0\n",
      "125 609 4 0.5 1.0\n",
      "125 609 5 0.5 1.0\n",
      "125 609 6 0.5 1.0\n",
      "125 609 7 0.5 1.0\n",
      "125 609 8 0.5 1.0\n",
      "125 609 9 0.5 1.0\n",
      "125 609 10 0.5 1.0\n",
      "125 609 11 0.5 1.0\n",
      "125 609 12 0.5 1.0\n",
      "125 609 13 0.5 1.0\n",
      "125 609 14 0.5 1.0\n",
      "125 609 15 0.5 1.0\n",
      "125 609 16 0.5 1.0\n",
      "125 609 17 0.5 1.0\n",
      "125 609 18 0.5 1.0\n",
      "125 609 19 0.5 1.0\n",
      "125 609 20 0.5 1.0\n",
      "125 609 21 0.5 1.0\n",
      "125 610 1 0.5 1.0\n",
      "125 610 2 0.5 1.0\n",
      "125 610 3 0.5 1.0\n",
      "125 610 4 0.5 1.0\n",
      "125 610 5 0.5 1.0\n",
      "125 610 6 0.5 1.0\n",
      "125 610 7 0.5 1.0\n",
      "125 610 8 0.5 1.0\n",
      "125 610 9 0.5 1.0\n",
      "125 610 10 0.5 1.0\n",
      "125 610 11 0.5 1.0\n",
      "125 610 12 0.5 1.0\n",
      "125 610 13 0.5 1.0\n",
      "125 610 14 0.5 1.0\n",
      "125 610 15 0.5 1.0\n",
      "125 610 16 0.5 1.0\n",
      "125 610 17 0.5 1.0\n",
      "125 610 18 0.5 1.0\n",
      "125 610 19 0.5 1.0\n",
      "125 610 20 0.5 1.0\n",
      "125 610 21 0.5 1.0\n",
      "125 611 1 0.5 1.0\n",
      "125 611 2 0.5 1.0\n",
      "125 611 3 0.5 1.0\n",
      "125 611 4 0.5 1.0\n",
      "125 611 5 0.5 1.0\n",
      "125 611 6 0.5 1.0\n",
      "125 611 7 0.5 1.0\n",
      "125 611 8 0.5 1.0\n",
      "125 611 9 0.5 1.0\n",
      "125 611 10 0.5 1.0\n",
      "125 611 11 0.5 1.0\n",
      "125 611 12 0.5 1.0\n",
      "125 611 13 0.5 1.0\n",
      "125 611 14 0.5 1.0\n",
      "125 611 15 0.5 1.0\n",
      "125 611 16 0.5 1.0\n",
      "125 611 17 0.5 1.0\n",
      "125 611 18 0.5 1.0\n",
      "125 611 19 0.5 1.0\n",
      "125 611 20 0.5 1.0\n",
      "125 612 1 0.5 1.0\n",
      "125 612 2 0.5 1.0\n",
      "125 612 3 0.5 1.0\n",
      "125 612 4 0.5 1.0\n",
      "125 612 5 0.5 1.0\n",
      "125 612 6 0.5 1.0\n",
      "125 612 7 0.5 1.0\n",
      "125 612 8 0.5 1.0\n",
      "125 612 9 0.5 1.0\n",
      "125 612 10 0.5 1.0\n",
      "125 612 11 0.5 1.0\n",
      "125 612 12 0.5 1.0\n",
      "125 629 1 0.5 1.0\n",
      "125 629 2 0.5 1.0\n",
      "125 629 3 0.5 1.0\n",
      "125 629 4 0.5 1.0\n",
      "125 629 5 0.5 1.0\n",
      "125 629 6 0.5 1.0\n",
      "125 629 7 0.5 1.0\n",
      "126 599 1 0.5 1.0\n",
      "126 599 2 0.5 1.0\n",
      "126 599 3 0.5 1.0\n",
      "126 599 4 0.5 1.0\n",
      "126 599 5 0.5 1.0\n",
      "126 599 6 0.5 1.0\n",
      "126 599 7 0.5 1.0\n",
      "126 599 8 0.5 1.0\n",
      "126 599 9 0.5 1.0\n",
      "126 599 10 0.5 1.0\n",
      "126 599 11 0.5 1.0\n",
      "126 600 1 0.5 1.0\n",
      "126 600 2 0.5 1.0\n",
      "126 600 3 0.5 1.0\n",
      "126 600 4 0.5 1.0\n",
      "126 600 5 0.5 1.0\n",
      "126 600 6 0.5 1.0\n",
      "126 600 7 0.5 1.0\n",
      "126 600 8 0.5 1.0\n",
      "126 600 9 0.5 1.0\n",
      "126 600 10 0.5 1.0\n",
      "126 600 11 0.5 1.0\n",
      "126 600 12 0.5 1.0\n",
      "126 600 13 0.5 1.0\n",
      "126 600 14 0.5 1.0\n",
      "126 600 15 0.5 1.0\n",
      "126 600 16 0.5 1.0\n",
      "126 600 17 0.5 1.0\n",
      "126 600 18 0.5 1.0\n",
      "126 600 19 0.5 1.0\n",
      "126 601 1 0.5 1.0\n",
      "126 601 2 0.5 1.0\n",
      "126 601 3 0.5 1.0\n",
      "126 601 4 0.5 1.0\n",
      "126 601 5 0.5 1.0\n",
      "126 601 6 0.5 1.0\n",
      "126 601 7 0.5 1.0\n",
      "126 601 8 0.5 1.0\n",
      "126 601 9 0.5 1.0\n",
      "126 601 10 0.5 1.0\n",
      "126 601 11 0.5 1.0\n",
      "126 601 12 0.5 1.0\n",
      "126 601 13 0.5 1.0\n",
      "126 601 14 0.5 1.0\n",
      "126 601 15 0.5 1.0\n",
      "126 601 16 0.5 1.0\n",
      "126 601 17 0.5 1.0\n",
      "126 601 18 0.5 1.0\n",
      "126 601 19 0.5 1.0\n",
      "126 601 20 0.5 1.0\n",
      "126 601 21 0.5 1.0\n",
      "126 602 1 0.5 1.0\n",
      "126 602 2 0.5 1.0\n",
      "126 602 3 0.5 1.0\n",
      "126 602 4 0.5 1.0\n",
      "126 602 5 0.5 1.0\n",
      "126 602 6 0.5 1.0\n",
      "126 602 7 0.5 1.0\n",
      "126 602 8 0.5 1.0\n",
      "126 602 9 0.5 1.0\n",
      "126 602 10 0.5 1.0\n",
      "126 602 11 0.5 1.0\n",
      "126 602 12 0.5 1.0\n",
      "126 602 13 0.5 1.0\n",
      "126 602 14 0.5 1.0\n",
      "126 602 15 0.5 1.0\n",
      "126 602 16 0.5 1.0\n",
      "126 602 17 0.5 1.0\n",
      "126 602 18 0.5 1.0\n",
      "126 602 19 0.5 1.0\n",
      "126 602 20 0.5 1.0\n",
      "126 602 21 0.5 1.0\n",
      "126 603 1 0.5 1.0\n",
      "126 603 2 0.5 1.0\n",
      "126 603 3 0.5 1.0\n",
      "126 603 4 0.5 1.0\n",
      "126 603 5 0.5 1.0\n",
      "126 603 6 0.5 1.0\n",
      "126 603 7 0.5 1.0\n",
      "126 603 8 0.5 1.0\n",
      "126 603 9 0.5 1.0\n",
      "126 603 10 0.5 1.0\n",
      "126 603 11 0.5 1.0\n",
      "126 603 12 0.5 1.0\n",
      "126 603 13 0.5 1.0\n",
      "126 603 14 0.5 1.0\n",
      "126 603 15 0.5 1.0\n",
      "126 603 16 0.5 1.0\n",
      "126 603 17 0.5 1.0\n",
      "126 603 18 0.5 1.0\n",
      "126 603 19 0.5 1.0\n",
      "126 603 20 0.5 1.0\n",
      "126 603 21 0.5 1.0\n",
      "126 604 1 0.5 1.0\n",
      "126 604 2 0.5 1.0\n",
      "126 604 3 0.5 1.0\n",
      "126 604 4 0.5 1.0\n",
      "126 604 5 0.5 1.0\n",
      "126 604 6 0.5 1.0\n",
      "126 604 7 0.5 1.0\n",
      "126 604 8 0.5 1.0\n",
      "126 604 9 0.5 1.0\n",
      "126 604 10 0.5 1.0\n",
      "126 604 11 0.5 1.0\n",
      "126 604 12 0.5 1.0\n",
      "126 604 13 0.5 1.0\n",
      "126 604 14 0.5 1.0\n",
      "126 604 15 0.5 1.0\n",
      "126 604 16 0.5 1.0\n",
      "126 604 17 0.5 1.0\n",
      "126 604 18 0.5 1.0\n",
      "126 604 19 0.5 1.0\n",
      "126 604 20 0.5 1.0\n",
      "126 605 1 0.5 1.0\n",
      "126 605 2 0.5 1.0\n",
      "126 605 3 0.5 1.0\n",
      "126 605 4 0.5 1.0\n",
      "126 605 5 0.5 1.0\n",
      "126 605 6 0.5 1.0\n",
      "126 605 7 0.5 1.0\n",
      "126 605 8 0.5 1.0\n",
      "126 605 9 0.5 1.0\n",
      "126 605 10 0.5 1.0\n",
      "126 605 11 0.5 1.0\n",
      "126 605 12 0.5 1.0\n",
      "126 605 13 0.5 1.0\n",
      "126 605 14 0.5 1.0\n",
      "126 605 15 0.5 1.0\n",
      "126 605 16 0.5 1.0\n",
      "126 605 17 0.5 1.0\n",
      "126 605 18 0.5 1.0\n",
      "126 606 1 0.5 1.0\n",
      "126 606 2 0.5 1.0\n",
      "126 606 3 0.5 1.0\n",
      "126 606 4 0.5 1.0\n",
      "126 606 5 0.5 1.0\n",
      "126 606 6 0.5 1.0\n",
      "126 606 7 0.5 1.0\n",
      "126 606 8 0.5 1.0\n",
      "126 606 9 0.5 1.0\n",
      "126 606 10 0.5 1.0\n",
      "126 606 11 0.5 1.0\n",
      "126 606 12 0.5 1.0\n",
      "126 606 13 0.5 1.0\n",
      "126 606 14 0.5 1.0\n",
      "126 606 15 0.5 1.0\n",
      "126 606 16 0.5 1.0\n",
      "126 606 17 0.5 1.0\n",
      "126 606 18 0.5 1.0\n",
      "126 606 19 0.5 1.0\n",
      "126 607 1 0.5 1.0\n",
      "126 607 2 0.5 1.0\n",
      "126 607 3 0.5 1.0\n",
      "126 607 4 0.5 1.0\n",
      "126 607 5 0.5 1.0\n",
      "126 607 6 0.5 1.0\n",
      "126 607 7 0.5 1.0\n",
      "126 607 8 0.5 1.0\n",
      "126 607 9 0.5 1.0\n",
      "126 607 10 0.5 1.0\n",
      "126 607 11 0.5 1.0\n",
      "126 607 12 0.5 1.0\n",
      "126 607 13 0.5 1.0\n",
      "126 607 14 0.5 1.0\n",
      "126 607 15 0.5 1.0\n",
      "126 607 16 0.5 1.0\n",
      "126 607 17 0.5 1.0\n",
      "126 607 18 0.5 1.0\n",
      "126 608 1 0.5 1.0\n",
      "126 608 2 0.5 1.0\n",
      "126 608 3 0.5 1.0\n",
      "126 608 4 0.5 1.0\n",
      "126 608 5 0.5 1.0\n",
      "126 608 6 0.5 1.0\n",
      "126 608 7 0.5 1.0\n",
      "126 608 8 0.5 1.0\n",
      "126 608 9 0.5 1.0\n",
      "126 608 10 0.5 1.0\n",
      "126 608 11 0.5 1.0\n",
      "126 608 12 0.5 1.0\n",
      "126 608 13 0.5 1.0\n",
      "126 608 14 0.5 1.0\n",
      "126 608 15 0.5 1.0\n",
      "126 608 16 0.5 1.0\n",
      "126 608 17 0.5 1.0\n",
      "126 608 18 0.5 1.0\n",
      "126 608 19 0.5 1.0\n",
      "126 609 1 0.5 1.0\n",
      "126 609 2 0.5 1.0\n",
      "126 609 3 0.5 1.0\n",
      "126 609 4 0.5 1.0\n",
      "126 609 5 0.5 1.0\n",
      "126 609 6 0.5 1.0\n",
      "126 609 7 0.5 1.0\n",
      "126 609 8 0.5 1.0\n",
      "126 609 9 0.5 1.0\n",
      "126 609 10 0.5 1.0\n",
      "126 609 11 0.5 1.0\n",
      "126 609 12 0.5 1.0\n",
      "126 609 13 0.5 1.0\n",
      "126 609 14 0.5 1.0\n",
      "126 609 15 0.5 1.0\n",
      "126 609 16 0.5 1.0\n",
      "126 609 17 0.5 1.0\n",
      "126 609 18 0.5 1.0\n",
      "127 599 1 0.5 1.0\n",
      "127 599 2 0.5 1.0\n",
      "127 599 3 0.5 1.0\n",
      "127 599 4 0.5 1.0\n",
      "127 599 5 0.5 1.0\n",
      "127 599 6 0.5 1.0\n",
      "127 599 7 0.5 1.0\n",
      "127 599 8 0.5 1.0\n",
      "127 600 1 0.5 1.0\n",
      "127 600 2 0.5 1.0\n",
      "127 600 3 0.5 1.0\n",
      "127 600 4 0.5 1.0\n",
      "127 600 5 0.5 1.0\n",
      "127 600 6 0.5 1.0\n",
      "127 600 7 0.5 1.0\n",
      "127 600 8 0.5 1.0\n",
      "127 600 9 0.5 1.0\n",
      "127 600 10 0.5 1.0\n",
      "127 600 11 0.5 1.0\n",
      "127 600 12 0.5 1.0\n",
      "127 600 13 0.5 1.0\n",
      "127 600 14 0.5 1.0\n",
      "127 600 15 0.5 1.0\n",
      "127 600 16 0.5 1.0\n",
      "127 600 17 0.5 1.0\n",
      "127 601 1 0.5 1.0\n",
      "127 601 2 0.5 1.0\n",
      "127 601 3 0.5 1.0\n",
      "127 601 4 0.5 1.0\n",
      "127 601 5 0.5 1.0\n",
      "127 601 6 0.5 1.0\n",
      "127 601 7 0.5 1.0\n",
      "127 601 8 0.5 1.0\n",
      "127 601 9 0.5 1.0\n",
      "127 601 10 0.5 1.0\n",
      "127 601 11 0.5 1.0\n",
      "127 601 12 0.5 1.0\n",
      "127 601 13 0.5 1.0\n",
      "127 601 14 0.5 1.0\n",
      "127 601 15 0.5 1.0\n",
      "127 601 16 0.5 1.0\n",
      "127 601 17 0.5 1.0\n",
      "127 601 18 0.5 1.0\n",
      "127 601 19 0.5 1.0\n",
      "127 601 20 0.5 1.0\n",
      "127 601 21 0.5 1.0\n",
      "127 602 1 0.5 1.0\n",
      "127 602 2 0.5 1.0\n",
      "127 602 3 0.5 1.0\n",
      "127 602 4 0.5 1.0\n",
      "127 602 5 0.5 1.0\n",
      "127 602 6 0.5 1.0\n",
      "127 602 7 0.5 1.0\n",
      "127 602 8 0.5 1.0\n",
      "127 602 9 0.5 1.0\n",
      "127 602 10 0.5 1.0\n",
      "127 602 11 0.5 1.0\n",
      "127 602 12 0.5 1.0\n",
      "127 602 13 0.5 1.0\n",
      "127 602 14 0.5 1.0\n",
      "127 602 15 0.5 1.0\n",
      "127 602 16 0.5 1.0\n",
      "127 602 17 0.5 1.0\n",
      "127 602 18 0.5 1.0\n",
      "127 603 1 0.5 1.0\n",
      "127 603 2 0.5 1.0\n",
      "127 603 3 0.5 1.0\n",
      "127 603 4 0.5 1.0\n",
      "127 603 5 0.5 1.0\n",
      "127 603 6 0.5 1.0\n",
      "127 603 7 0.5 1.0\n",
      "127 603 8 0.5 1.0\n",
      "127 603 9 0.5 1.0\n",
      "127 603 10 0.5 1.0\n",
      "127 603 11 0.5 1.0\n",
      "127 603 12 0.5 1.0\n",
      "127 604 1 0.5 1.0\n",
      "127 604 2 0.5 1.0\n",
      "127 604 3 0.5 1.0\n",
      "127 604 4 0.5 1.0\n",
      "127 604 5 0.5 1.0\n",
      "127 604 6 0.5 1.0\n",
      "127 604 7 0.5 1.0\n",
      "127 604 8 0.5 1.0\n",
      "127 604 9 0.5 1.0\n",
      "127 604 10 0.5 1.0\n",
      "127 604 11 0.5 1.0\n",
      "127 604 12 0.5 1.0\n",
      "128 600 1 0.5 1.0\n",
      "128 600 2 0.5 1.0\n",
      "128 600 3 0.5 1.0\n",
      "128 600 4 0.5 1.0\n",
      "128 600 5 0.5 1.0\n",
      "128 600 6 0.5 1.0\n",
      "128 600 7 0.5 1.0\n",
      "128 600 8 0.5 1.0\n",
      "128 600 9 0.5 1.0\n",
      "128 601 1 0.5 1.0\n",
      "128 601 2 0.5 1.0\n",
      "128 601 3 0.5 1.0\n",
      "128 601 4 0.5 1.0\n",
      "128 601 5 0.5 1.0\n",
      "128 601 6 0.5 1.0\n",
      "128 601 7 0.5 1.0\n",
      "128 601 8 0.5 1.0\n",
      "128 601 9 0.5 1.0\n",
      "128 601 10 0.5 1.0\n",
      "128 601 11 0.5 1.0\n",
      "128 601 12 0.5 1.0\n",
      "128 601 13 0.5 1.0\n",
      "128 601 14 0.5 1.0\n",
      "128 601 15 0.5 1.0\n",
      "128 601 16 0.5 1.0\n",
      "128 601 17 0.5 1.0\n",
      "128 602 1 0.5 1.0\n",
      "128 602 2 0.5 1.0\n",
      "128 602 3 0.5 1.0\n",
      "128 602 4 0.5 1.0\n",
      "128 602 5 0.5 1.0\n",
      "128 602 6 0.5 1.0\n",
      "128 602 7 0.5 1.0\n",
      "128 602 8 0.5 1.0\n",
      "128 602 9 0.5 1.0\n",
      "128 602 10 0.5 1.0\n",
      "129 599 1 0.5 1.0\n",
      "129 599 2 0.5 1.0\n",
      "129 599 3 0.5 1.0\n",
      "129 600 1 0.5 1.0\n",
      "129 600 2 0.5 1.0\n",
      "129 600 3 0.5 1.0\n",
      "129 600 4 0.5 1.0\n",
      "129 600 5 0.5 1.0\n",
      "129 601 1 0.5 1.0\n",
      "129 601 2 0.5 1.0\n",
      "129 601 3 0.5 1.0\n",
      "129 601 4 0.5 1.0\n",
      "129 601 5 0.5 1.0\n",
      "129 601 6 0.5 1.0\n",
      "129 601 7 0.5 1.0\n",
      "129 601 8 0.5 1.0\n",
      "129 601 9 0.5 1.0\n",
      "129 601 10 0.5 1.0\n"
     ]
    }
   ],
   "source": [
    "for x in np.arange(120,133):\n",
    "        for y in np.arange(599,630):\n",
    "            for z in range(1,40): \n",
    "                if (mesh_mask.variables['tmask'][0,z,y,x] == 1) and (mesh_mask.variables['tmask'][0,z+2,y,x] == 1) and (mesh_mask.variables['tmask'][0,z,y+1,x] == 1) and (mesh_mask.variables['tmask'][0,z,y,x+1] == 1) and (mesh_mask.variables['tmask'][0,z,y-1,x] == 1) and (mesh_mask.variables['tmask'][0,z,y,x-1] == 1):\n",
    "                    print(str(x) +' ' + str(y) +' '+ str(z)+ ' 0.5 1.0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with open('initial_positions.txt', 'a') as f:\n",
    "    for x in np.arange(120,133):\n",
    "        for y in np.arange(598,608):\n",
    "            for z in range(2,40):\n",
    "                if ((mesh_mask.variables['tmask'][0,z,y,x] == 1) and (mesh_mask.variables['tmask'][0,z+2,y,x] == 1) \n",
    "                and (mesh_mask.variables['tmask'][0,z,y+1,x] == 1) and (mesh_mask.variables['tmask'][0,z,y,x+1] == 1) \n",
    "                and (mesh_mask.variables['tmask'][0,z,y-1,x] == 1) and (mesh_mask.variables['tmask'][0,z,y,x-1] == 1)\n",
    "                and (mesh_mask.variables['tmask'][0,z+2,y,x+1] == 1) and (mesh_mask.variables['tmask'][0,z+2,y,x-1] == 1)\n",
    "               and (mesh_mask.variables['tmask'][0,z+2,y-1,x] == 1) and (mesh_mask.variables['tmask'][0,z+2,y+1,x] == 1)\n",
    "               and (mesh_mask.variables['tmask'][0,z+2,y-1,x-1] == 1) and (mesh_mask.variables['tmask'][0,z+2,y-1,x+1] == 1)\n",
    "               and (mesh_mask.variables['tmask'][0,z+2,y+1,x-1] == 1) and (mesh_mask.variables['tmask'][0,z+2,y+1,x+1] == 1)):\n",
    "                    f.write((str(x) +' ' + str(y) +' '+ str(z+0.5)+ ' 0.5 1.0\\n'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "407"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAD8CAYAAABJsn7AAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0e2a8fb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(1,1)\n",
    "mesh = ax.pcolormesh(mesh_mask.variables['tmask'][0,:,620,:])\n",
    "ax.set_ylim((30,0))\n",
    "ax.set_xlim((118,133))\n",
    "fig.colorbar(mesh,ax=ax)\n",
    "ax.plot(120,12,'r*')\n",
    "plt.grid('on')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int8)"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mesh_mask.variables['tmask'][0,:,614,120]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "test = nc.Dataset('/ocean/vdo/MEOPAR/ariane-runs/test/ariane_trajectories_qualitative.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with open('initial_positions.txt', 'a') as f:\n",
    "    for x in np.arange(120,130):\n",
    "        for y in np.arange(735,750):\n",
    "            for z in range(2,40):\n",
    "                for t in range(1,30):\n",
    "                    if ((mesh_mask.variables['tmask'][0,z,y,x] == 1) and (mesh_mask.variables['tmask'][0,z+2,y,x] == 1) \n",
    "                    and (mesh_mask.variables['tmask'][0,z,y+1,x] == 1) and (mesh_mask.variables['tmask'][0,z,y,x+1] == 1) \n",
    "                    and (mesh_mask.variables['tmask'][0,z,y-1,x] == 1) and (mesh_mask.variables['tmask'][0,z,y,x-1] == 1)\n",
    "                    and (mesh_mask.variables['tmask'][0,z+2,y,x+1] == 1) and (mesh_mask.variables['tmask'][0,z+2,y,x-1] == 1)\n",
    "                   and (mesh_mask.variables['tmask'][0,z+2,y-1,x] == 1) and (mesh_mask.variables['tmask'][0,z+2,y+1,x] == 1)\n",
    "                   and (mesh_mask.variables['tmask'][0,z+2,y-1,x-1] == 1) and (mesh_mask.variables['tmask'][0,z+2,y-1,x+1] == 1)\n",
    "                   and (mesh_mask.variables['tmask'][0,z+2,y+1,x-1] == 1) and (mesh_mask.variables['tmask'][0,z+2,y+1,x+1] == 1)):\n",
    "                        f.write((str(x) +' ' + str(y) +' '+ str(z+0.5)+' '+ str(t)+' 1.0\\n'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Jan04 = nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/SalishSeaLake/LakeJan0.4/SalishSea_1h_20170101_20170107_grid_T.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   0.5000003 ,    1.5000031 ,    2.50001144,    3.50003052,\n",
       "          4.50007057,    5.50015068,    6.50031042,    7.50062323,\n",
       "          8.50123596,    9.50243282,   10.50476551,   11.50931168,\n",
       "         12.51816654,   13.53541183,   14.56898212,   15.63428783,\n",
       "         16.76117325,   18.00713539,   19.48178482,   21.38997841,\n",
       "         24.10025597,   28.22991562,   34.68575668,   44.51772308,\n",
       "         58.48433304,   76.58558655,   98.06295776,  121.86651611,\n",
       "        147.08946228,  173.11448669,  199.57304382,  226.26029968,\n",
       "        253.06663513,  279.93453979,  306.834198  ,  333.75018311,\n",
       "        360.67453003,  387.60321045,  414.53408813,  441.46609497], dtype=float32)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Jan04.variables['deptht'][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  24.10025597,   28.22991562,   34.68575668,   44.51772308,\n",
       "         58.48433304,   76.58558655,   98.06295776,  121.86651611,\n",
       "        147.08946228,  173.11448669,  199.57304382,  226.26029968,\n",
       "        253.06663513,  279.93453979,  306.834198  ,  333.75018311,\n",
       "        360.67453003,  387.60321045,  414.53408813,  441.46609497], dtype=float32)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Jan04.variables['deptht'][20:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('initial_positions2.txt', 'a') as f:\n",
    "    for x in np.arange(118,133):\n",
    "        for y in np.arange(608,659):\n",
    "            for z in range(21,30):\n",
    "                if ((mesh_mask.variables['tmask'][0,z,y,x] == 1) and (mesh_mask.variables['tmask'][0,z+1,y,x] == 1) \n",
    "                and (mesh_mask.variables['tmask'][0,z,y+1,x] == 1) and (mesh_mask.variables['tmask'][0,z,y,x+1] == 1) \n",
    "                and (mesh_mask.variables['tmask'][0,z,y-1,x] == 1) and (mesh_mask.variables['tmask'][0,z,y,x-1] == 1)\n",
    "                and (mesh_mask.variables['tmask'][0,z+1,y,x+1] == 1) and (mesh_mask.variables['tmask'][0,z+1,y,x-1] == 1)\n",
    "               and (mesh_mask.variables['tmask'][0,z+1,y-1,x] == 1) and (mesh_mask.variables['tmask'][0,z+1,y+1,x] == 1)\n",
    "               and (mesh_mask.variables['tmask'][0,z+1,y-1,x-1] == 1) and (mesh_mask.variables['tmask'][0,z+1,y-1,x+1] == 1)\n",
    "               and (mesh_mask.variables['tmask'][0,z+1,y+1,x-1] == 1) and (mesh_mask.variables['tmask'][0,z+1,y+1,x+1] == 1)):\n",
    "                    f.write((str(x) +' ' + str(y) +' '+ str(z+0.5)+ ' 0.5 1.0\\n'))\n",
    "                    f.write((str(x+0.25) +' ' + str(y) +' '+ str(z+0.5)+ ' 0.5 1.0\\n'))\n",
    "                    f.write((str(x+0.5) +' ' + str(y) +' '+ str(z+0.5)+ ' 0.5 1.0\\n'))\n",
    "                    f.write((str(x+0.75) +' ' + str(y) +' '+ str(z+0.5)+ ' 0.5 1.0\\n'))\n",
    "                    f.write((str(x) +' ' + str(y+0.5) +' '+ str(z+0.5)+ ' 0.5 1.0\\n'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.variables['Bathymetry'][608:659, 118:133].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for x in np.arange(118,133):\n",
    "    for y in np.arange(608,659):\n",
    "        if grid.variables['Bathymetry'][y,x] >= 30:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "July = nc.Dataset('/results/SalishSea/hindcast/01jul17/SalishSea_1h_20170701_20170701_grid_T.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  28.22991562,   34.68575668,   44.51772308,   58.48433304,\n",
       "         76.58558655,   98.06295776,  121.86651611,  147.08946228,\n",
       "        173.11448669,  199.57304382,  226.26029968,  253.06663513,\n",
       "        279.93453979,  306.834198  ,  333.75018311,  360.67453003,\n",
       "        387.60321045,  414.53408813,  441.46609497], dtype=float32)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "July.variables['deptht'][:][21:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with open('initial_positions3.txt', 'a') as f:\n",
    "    for x in np.arange(118,133):\n",
    "        for y in np.arange(608,659):\n",
    "            for z in range(21,32):\n",
    "                if ((mesh_mask.variables['tmask'][0,z,y,x] == 1) \n",
    "                and (mesh_mask.variables['tmask'][0,z,y+1,x] == 1) and (mesh_mask.variables['tmask'][0,z,y,x+1] == 1) \n",
    "                and (mesh_mask.variables['tmask'][0,z,y-1,x] == 1) and (mesh_mask.variables['tmask'][0,z,y,x-1] == 1)):\n",
    "                    f.write((str(x) +' ' + str(y) +' '+ str(z+0.5)+ ' 0.5 1.0\\n'))\n",
    "                    f.write((str(x+0.25) +' ' + str(y) +' '+ str(z+0.5)+ ' 0.5 1.0\\n'))\n",
    "                    f.write((str(x+0.5) +' ' + str(y) +' '+ str(z+0.5)+ ' 0.5 1.0\\n'))\n",
    "                    f.write((str(x+0.75) +' ' + str(y) +' '+ str(z+0.5)+ ' 0.5 1.0\\n'))\n",
    "                    f.write((str(x) +' ' + str(y+0.5) +' '+ str(z+0.5)+ ' 0.5 1.0\\n'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "grid = nc.Dataset('/data/vdo/MEOPAR/NEMO-forcing/grid/bathymetry_201702.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.patches.Rectangle at 0x7f98c978cc50>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAMEAAAKvCAYAAADTI6ufAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f98c98e2438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize=(16,12))\n",
    "bathy = ax.pcolormesh(grid.variables['Bathymetry'][:])\n",
    "ax.set_ylim(590,670)\n",
    "ax.set_xlim(115,135)\n",
    "viz_tools.set_aspect(ax)\n",
    "ax.add_patch(patches.Rectangle((118,598), 13, 18, fill=False, linewidth=3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('initial_positions2.txt', 'a') as f:\n",
    "    for x in np.arange(118,132):\n",
    "        for y in np.arange(598,617):\n",
    "            for z in range(21,26):\n",
    "                if ((mesh_mask.variables['tmask'][0,z,y,x] == 1) \n",
    "                and (mesh_mask.variables['tmask'][0,z,y+1,x] == 1) and (mesh_mask.variables['tmask'][0,z,y,x+1] == 1) \n",
    "                and (mesh_mask.variables['tmask'][0,z,y-1,x] == 1) and (mesh_mask.variables['tmask'][0,z,y,x-1] == 1)):\n",
    "                    f.write((str(x) +' ' + str(y) +' '+ str(z+0.5)+ ' 0.5 1.0\\n'))\n",
    "                    f.write((str(x+0.25) +' ' + str(y) +' '+ str(z+0.5)+ ' 0.5 1.0\\n'))\n",
    "                    f.write((str(x+0.5) +' ' + str(y) +' '+ str(z+0.5)+ ' 0.5 1.0\\n'))\n",
    "                    f.write((str(x+0.75) +' ' + str(y) +' '+ str(z+0.5)+ ' 0.5 1.0\\n'))\n",
    "                    f.write((str(x) +' ' + str(y+0.5) +' '+ str(z+0.5)+ ' 0.5 1.0\\n'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('initial_positions2.txt', 'a') as f:\n",
    "    for x in np.arange(118,132):\n",
    "        for y in np.arange(598,617):\n",
    "            for z in range(21,26):\n",
    "                if ((mesh_mask.variables['tmask'][0,z,y,x] == 1) \n",
    "                and (mesh_mask.variables['tmask'][0,z,y+1,x] == 1) and (mesh_mask.variables['tmask'][0,z,y,x+1] == 1) \n",
    "                and (mesh_mask.variables['tmask'][0,z,y-1,x] == 1) and (mesh_mask.variables['tmask'][0,z,y,x-1] == 1)):\n",
    "                    f.write((str(x) +' ' + str(y) +' '+ str(z+0.5)+ ' 0.5 1.0\\n'))\n",
    "                    f.write((str(x+0.25) +' ' + str(y) +' '+ str(z+0.5)+ ' 0.5 1.0\\n'))\n",
    "                    f.write((str(x+0.5) +' ' + str(y) +' '+ str(z+0.5)+ ' 0.5 1.0\\n'))\n",
    "                    f.write((str(x+0.75) +' ' + str(y) +' '+ str(z+0.5)+ ' 0.5 1.0\\n'))\n",
    "                    f.write((str(x) +' ' + str(y+0.5) +' '+ str(z+0.5)+ ' 0.5 1.0\\n'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[24]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(range(24,25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with open('initial_positions2.txt', 'a') as f:\n",
    "    for x in np.arange(118,132):\n",
    "        for y in np.arange(598,617):\n",
    "            for z in range(24,26):\n",
    "                if ((mesh_mask.variables['tmask'][0,z,y,x]) == 1):\n",
    "                    f.write((str(x) +' ' + str(y) +' '+ str(z+0.5)+ ' 0.5 1.0\\n'))\n",
    "                    f.write((str(x+0.25) +' ' + str(y) +' '+ str(z+0.5)+ ' 0.5 1.0\\n'))\n",
    "                    f.write((str(x+0.5) +' ' + str(y) +' '+ str(z+0.5)+ ' 0.5 1.0\\n'))\n",
    "                    f.write((str(x+0.75) +' ' + str(y) +' '+ str(z+0.5)+ ' 0.5 1.0\\n'))\n",
    "                    f.write((str(x) +' ' + str(y+0.5) +' '+ str(z+0.5)+ ' 0.5 1.0\\n'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}