{
 "metadata": {
  "name": "",
  "signature": "sha256:fcd869cfcc1500329960daf15113b3ee0cfa5981a560e25a262ce102d437f948"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Salinity ferry data template"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Daily salinity comparsion between NEMO model results and ferry data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from __future__ import division, print_function\n",
      "from salishsea_tools import (nc_tools,viz_tools,stormtools,tidetools)\n",
      "from datetime import datetime, timedelta\n",
      "from pylab import *\n",
      "from sklearn import linear_model\n",
      "import matplotlib.pyplot as plt\n",
      "import scipy.io as sio\n",
      "import netCDF4 as nc\n",
      "import numpy as np\n",
      "import math\n",
      "import glob\n",
      "import os\n",
      "import datetime"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Load ferry salinity and utc time"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "saline=sio.loadmat('/ocean/jieliu/research/meopar/autodataupdate/ferrydata/SBE1920150121.mat') #####need to change each time \n",
      "a=saline['ferryData']\n",
      "b=a['data']\n",
      "dataa = b[0,0]\n",
      "time=dataa['matlabtime'][0,0]\n",
      "lonn=dataa['Longitude'][0,0]\n",
      "latt=dataa['Latitude'][0,0]\n",
      "salinity=dataa['Practical_Salinity'][0,0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a=len(time)\n",
      "lon1=np.zeros([a,1])\n",
      "lat1=np.zeros([a,1])\n",
      "salinity1=np.zeros([a,1])\n",
      "for i in np.arange(0,a):\n",
      "    matlab_datenum = np.float(time[i])\n",
      "    python_datetime = datetime.datetime.fromordinal(int(matlab_datenum)) + timedelta(days=matlab_datenum%1) - timedelta(days = 366)\n",
      "    if((python_datetime.year == 2015) & (python_datetime.month == 1) & (python_datetime.day == 22) & (python_datetime.hour >= 2))&(python_datetime.hour <= 3):\n",
      "        lon1[i]=lonn[i]\n",
      "        lat1[i]=latt[i]\n",
      "        salinity1[i]=salinity[i]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mask=lon1[:,0]!=0\n",
      "lon1_2_4=lon1[mask]\n",
      "lat1_2_4=lat1[mask]\n",
      "salinity1_2_4=salinity1[mask]\n",
      "lon11=lon1_2_4[0:-1:50]\n",
      "lat11=lat1_2_4[0:-1:50]\n",
      "salinity11=salinity1_2_4[0:-1:50]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def date(year, month, day_start, day_end, period, grid):\n",
      "    \n",
      "    day_range = np.arange(day_start, day_end+1)\n",
      "    day_len = len(day_range)\n",
      "    files_all = [None] * day_len\n",
      "    inds = np.arange(day_len)\n",
      "    \n",
      "    for i, day in zip(inds, day_range):\n",
      "        run_date = datetime.datetime(year,month, day)\n",
      "        results_home = '/data/dlatorne/MEOPAR/SalishSea/nowcast/'\n",
      "        results_dir = os.path.join(results_home, run_date.strftime('%d%b%y').lower())\n",
      "        filename = 'SalishSea_' + period + '_' + run_date.strftime('%Y%m%d').lower() + '_' + run_date.strftime('%Y%m%d').lower() + '_' + grid + '.nc'\n",
      "        file_single = os.path.join(results_dir, filename)\n",
      "        files_all[i] = file_single\n",
      "        \n",
      "    print(files_all)\n",
      "\n",
      "    return(files_all)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bathy, X, Y = tidetools.get_SS2_bathy_data()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def find_dist_3rd (q):\n",
      "    k=0\n",
      "    values =0\n",
      "    dist = np.zeros(9)\n",
      "    weights = np.zeros(9)\n",
      "    value=np.zeros(9)\n",
      "    regr =linear_model.LinearRegression()\n",
      "    regr.fit(lon11,lat11);\n",
      "    regr.coef_\n",
      "\n",
      "    [x1, j1] = tidetools.find_closest_model_point(lon11[q],regr.predict(lon11[q]),X,Y,bathy,lon_tol=0.0052,lat_tol=0.00210,allow_land=False)\n",
      "    for i in np.arange(x1-1,x1+2):\n",
      "        for j in np.arange(j1-1,j1+2):\n",
      "            dist[k]=tidetools.haversine(lon11[q],lat11[q],longitude[i,j],latitude[i,j])\n",
      "            weights[k]=1.0/dist[k]\n",
      "            value[k]=saline_nemo_3rd[i,j]*weights[k]\n",
      "            values=values+value[k]\n",
      "            k+=1\n",
      "    return values,weights\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def find_dist_4rd (q):\n",
      "    k=0\n",
      "    valuess=0\n",
      "    dist = np.zeros(9)\n",
      "    weights = np.zeros(9)\n",
      "    value=np.zeros(9)\n",
      "    regr =linear_model.LinearRegression()\n",
      "    regr.fit(lon11,lat11);\n",
      "    regr.coef_\n",
      "\n",
      "    [x1, j1] = tidetools.find_closest_model_point(lon11[q],regr.predict(lon11[q]),X,Y,bathy,lon_tol=0.0052,lat_tol=0.00210,allow_land=False)\n",
      "    for i in np.arange(x1-1,x1+2):\n",
      "        for j in np.arange(j1-1,j1+2):\n",
      "            dist[k]=tidetools.haversine(lon11[q],lat11[q],longitude[i,j],latitude[i,j])\n",
      "            weights[k]=1.0/dist[k]\n",
      "            value[k]=saline_nemo_4rd[i,j]*weights[k]\n",
      "            valuess=valuess+value[k]\n",
      "            k+=1\n",
      "    return valuess,weights"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def find_dist_5rd (q):\n",
      "    k=0\n",
      "    valuesss=0\n",
      "    dist = np.zeros(9)\n",
      "    weights = np.zeros(9)\n",
      "    value=np.zeros(9)\n",
      "    regr =linear_model.LinearRegression()\n",
      "    regr.fit(lon11,lat11);\n",
      "    regr.coef_\n",
      "\n",
      "    [x1, j1] =tidetools.find_closest_model_point(lon11[q],regr.predict(lon11[q]),X,Y,bathy,lon_tol=0.0052,lat_tol=0.00210,allow_land=False)\n",
      "    for i in np.arange(x1-1,x1+2):\n",
      "        for j in np.arange(j1-1,j1+2):\n",
      "            dist[k]=tidetools.haversine(lon11[q],lat11[q],longitude[i,j],latitude[i,j])\n",
      "            weights[k]=1.0/dist[k]\n",
      "            value[k]=saline_nemo_5rd[i,j]*weights[k]\n",
      "            valuesss=valuesss+value[k]\n",
      "            k+=1\n",
      "    return valuesss,weights"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Select NEMO model daily result"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "aa=date(2015,1,22,22,'1h','grid_T') ####need to change for different daily model results\n",
      "print(len(aa))\n",
      "tracers=nc.Dataset(aa[0])\n",
      "j=int(aa[0][65:67])\n",
      "jj=int(aa[0][67:69])\n",
      "latitude=tracers.variables['nav_lat'][:] \n",
      "longitude=tracers.variables['nav_lon'][:] \n",
      "saline_nemo = tracers.variables['vosaline']\n",
      "saline_nemo_3rd = saline_nemo[2,0, 0:898, 0:398] ####if change cell(12), then this should also change\n",
      "saline_nemo_4rd = saline_nemo[3,0, 0:898, 0:398] \n",
      "saline_nemo_5rd = saline_nemo[4,0, 0:898, 0:398] "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['/data/dlatorne/MEOPAR/SalishSea/nowcast/22jan15/SalishSea_1h_20150122_20150122_grid_T.nc']\n",
        "1\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "matrix=np.zeros([15,9])\n",
      "values=np.zeros([15,1])\n",
      "value_mean_3rd_hour=np.zeros([15,1])                      \n",
      "for q in np.arange(0,15):\n",
      "   values[q],matrix[q,:]=find_dist_3rd(q)\n",
      "   value_mean_3rd_hour[q]=values[q]/sum(matrix[q])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "matrixx=np.zeros([15,9])\n",
      "valuess=np.zeros([15,1])\n",
      "value_mean_4rd_hour=np.zeros([15,1])\n",
      "                            \n",
      "for q in np.arange(0,15):\n",
      "   valuess[q],matrixx[q,:]=find_dist_4rd(q)\n",
      "   value_mean_4rd_hour[q]=valuess[q]/sum(matrixx[q])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "matrixxx=np.zeros([15,9])\n",
      "valuesss=np.zeros([15,1])\n",
      "value_mean_5rd_hour=np.zeros([15,1]) \n",
      "                            \n",
      "for q in np.arange(0,15):\n",
      "   valuesss[q],matrixxx[q,:]=find_dist_5rd(q)\n",
      "   value_mean_5rd_hour[q]=valuesss[q]/sum(matrixxx[q])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Make the plot of comparisons between observation salinity and 2nd hour,3rd hour and 4th hour model results "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
      "model_salinity_3rd_hour=plt.plot(lon11,value_mean_3rd_hour,'r',label=\"model salinity 2nd hour\" )###sometimes need to change\n",
      "model_salinity_4rd_hour=plt.plot(lon11,value_mean_4rd_hour,'k',label=\"model salinity 3rd hour\" )\n",
      "model_salinity_5rd_hour=plt.plot(lon11,value_mean_5rd_hour,'g',label=\"model salinity 4th hour\" )\n",
      "observation_salinity=plt.plot(lon11,salinity11,'b',label=\"observation salinity\")\n",
      "\n",
      "ax.set_title(str(j)+'_'+str(jj)+'_2-4_surface_salinity_comparison')\n",
      "ax.set_xlabel('Longitude[degrees east]')\n",
      "ax.set_ylabel('Practical Salinity')\n",
      "ax.legend()\n",
      "locs,labels = xticks()\n",
      "xticks(locs, map(lambda x: \"%g\" % x, locs))\n",
      "plt.annotate('Observations from Ocean Networks Canada', (0.3,-0.1), (0, 10), xycoords='axes fraction', textcoords='offset points', va='top')\n",
      "fig.savefig(str(j)+'_'+str(jj)+'_2-4_surface_salinity_comparison.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "<matplotlib.text.Annotation at 0x7f318b107090>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAfUAAAIICAYAAAB3rHdLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSa3F/wc/LbfDlfV1bgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f318b1cb250>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}